Generation of novel metabolite-responsive transcription regulator biosensors

ABSTRACT

Disclosed are systems, components, and methods for sensing a ligand in a cell or a reaction mixture. The disclosed systems, components, and methods may include and/or utilize a fusion protein comprising a ligand-binding protein and a DNA-binding protein. The fusion protein binds the ligand of the ligand-binding protein and modulates expression of a reporter gene operably linked to a promoter that is engineered to include specific binding sites for the DNA-binding protein. The difference in expression of the reporter gene in the presence of the ligand versus expression of the reporter gene in the absence of the ligand can be correlated to the concentration of the ligand in a reaction mixture.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application No. 62/409,127, filed on Oct. 17,2016, the content of which is incorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under MCB1341414 awardedby the National Science Foundation, FP-91761101-0 awarded by theEnvironmental Protection Agency (EPA), and MH103910 awarded by theNational Institutes of Health. The government has certain rights in theinvention.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been filedelectronically in ASCII format and is hereby incorporated by referencein its entirety. Said ASCII copy, created on Jul. 12, 2019, is named121384-0133_SL.txt and is 31,170 bytes in size.

BACKGROUND

The field of the invention relates to biosensors comprising recombinantproteins and reporter systems. In particular, the field of the inventionrelates to biosensors comprising recombinant proteins that bind to aligand, such as a cellular metabolite, and then modulate transcriptionof a reporter based on binding to the ligand.

Efforts to engineer microbial factories have benefitted from miningbiological diversity and high throughput synthesis of novel enzymaticensembles, yet screening and optimizing metabolic pathways remainrate-limiting steps. Metabolite-responsive biosensors may help toaddress these persistent challenges by enabling the monitoring ofmetabolite levels in individual cells and the implementation ofmetabolite-responsive feedback control. We are currently limited tonaturally-evolved biosensors, which are insufficient for monitoring manymetabolites of interest. Thus, a method for engineering novel biosensorswould be powerful, yet we lack a generalizable approach that enables theconstruction of a wide range of biosensors. As a step towards this goal,we developed a bottom-up strategy for converting metabolite-bindingproteins into metabolite-responsive transcriptional regulators. Bypairing a modular protein design approach with a library of syntheticpromoters and applying robust statistical analyses, we identifiedquantitative design principles for engineering biosensor-regulatedpromoters and for achieving design-driven improvements of biosensorperformance. We demonstrated the feasibility of this strategy by fusinga programmable DNA binding motif (zinc finger module) with a modelligand binding protein (maltose binding protein), to generate a novelbiosensor conferring maltose-regulated gene expression. This technologyenables the design of novel biosensors for diverse synthetic biologyapplications.

SUMMARY

Disclosed are systems, components, and methods for sensing a ligand in acell or a reaction mixture. The disclosed systems, components, andmethods may include and/or utilize a fusion protein comprising aligand-binding protein and a DNA-binding protein that otherwise may bereferred to as a “biosensor.” The fusion protein, or biosensor, bindsthe ligand of the ligand-binding protein and modulates expression of areporter gene operably linked to a promoter that is engineered toinclude specific binding sites for the DNA-binding protein. Thedifference in expression of the reporter gene in the presence of theligand versus expression of the reporter gene in the absence of theligand can be correlated to the concentration of the ligand in thesystem. Also disclosed are recombinant methods for preparing andselecting fusion proteins that function as biosensors in the disclosedsystems and methods.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1. Developing novel zinc finger protein-regulated constitutivepromoters. (A) Crystal structure of a Cys₂-His₂ class of zinc fingerbinding to its cognate 9 bp DNA sequence (PDB #4R2A) (B) Repression ofthe constitutive promoter library by BCR-ABL1 (normalized to No Sitescontrol, which lacks BCR-ABL1 binding sites) (C) Relative expression ofthe top 5 most repressible promoters was evaluated during bothexponential growth (as in panel B) and after reaching stationary phase.(D and E) Select promoter constructs were evaluated by flow cytometry toassess variation in expression and repression across the population; oneplot representative of three biological replicates is shown for eachcondition. A concentration of 1% arabinose was used to induce theexpression of the BCR-ABL1 zinc finger. Relative expression is definedas the ratio of GFP/OD600 (for any given promoter) of the induced caserelative to that of the uninduced case, divided by this same ratio forthe No Sites promoter (a full description and rationale can be found inthe Materials and Methods section). Relative expression valuescalculated from these data are explicitly compared to comparablemicroplate assay-based metrics in FIG. 9. Microplate data were collectedover 7 sequential time points, spanning ˜1.5 h of mid-exponential phasegrowth, and averaged. All data represent mean values calculated fromthree independent experiments, and error bars represent one standarddeviation.

FIG. 2. Inspection-based evaluation of promoter design rules. Promoterswere manually grouped to represent exploration of design featuresincluding (A) presence and location of ZFP binding sites between the −10box and −35 region, (B) combinatorial effects of having a pair ofbinding sites between the −10 box and −35 region, (C) variations in thelocations of individual ZFP binding sites within the core, (D, E)contributions of additional BCR-ABL1 binding sites either downstream orupstream of the −10 box and −35 region, (F) spacing between the −10 boxand the downstream ZFP binding sites, (G) combinatorial effects ofdirectly flanking the −10 box and −35 region with ZFP binding sites. Alldata are re-plotted from FIG. 1B. Abbreviations and conventions: ZF isthe 9 bp BCR-ABL1 binding site; 1, 2, and 3 represent the first, second,or third, 3 bp finger binding sites within the BCR-ABL1 binding site;boxes represent spacer sequences of the indicated length (1, 2, 3, 4, 5,or 6 bp). Microplate data were collected over 7 sequential time points,spanning ˜1.5 h of mid-exponential phase growth, and averaged. All datarepresent mean values calculated from three independent experiments, anderror bars represent one standard deviation.

FIG. 3. Computational identification of promoter design featuresconferring ZFP-mediated repressibility (A) Shorthand names anddescriptions of the 17 features chosen to describe the promoter library.One binding site is defined as the nine base pair ZFP binding site. Onepair is defined as two adjacent binding site sequences. (B) PLSRanalysis of the degree to which promoter features explain variance inthe relative expression data (BCR-ABL-mediated repressibility) reportedin FIG. 1. Each series evaluates the explanatory power achieved using anincreasing number of features or principle components, each of which isadded to the set in ranked order from most to least important. (C) Lassoregression analysis of the degree to which promoter features explainvariance in the relative expression data reported in FIG. 1. This plotdisplays the number of features with non-zero coefficients (and theresulting mean squared error) as λ is increased, causing less importantfeatures to be eliminated from the regression analysis. The mean squarederror was obtained through 10-fold cross validation, which also produceda standard error for the mean squared error, which is shown as errorbars. (D) Relative importance of each feature, vis-à-vis explainingBCR-ABL1-mediated repressibility as determined by PLSR, Random Forest,and Lasso regression, with the overall order listed here determined byaverage rank across the three feature selection methods. (+)-positivecoefficients (large feature values confer more repressibility); and (−)negative coefficients (small feature values confer more repressibility).Detailed regression coefficients and importance values are provided inFIG. 10.

FIG. 4. Engineering novel biosensors using the split zinc finger (SZF)and Split Protein (SP) strategies. (A) This cartoon illustrates theproposed mechanism of action of an SZF biosensor. (B) SZF biosensorperformance, when paired with the reporter plasmids indicated, wasevaluated by inducing biosensor expression (30 μM IPTG) and evaluatingalleviation of repression upon addition of maltose (100 mM). Five graphbars from left to right: Reporter Only; Biosensor; Biosensor+Maltose;Biosensor+IPTG; and Biosensor+IPTG+Maltose. (C) This cartoon illustratesthe proposed mechanism of action of an SP biosensor. (D) SP biosensorperformance was evaluated as in panel B. Five graph bars from left toright: Reporter Only; Biosensor; Biosensor+Maltose; Biosensor+IPTG; andBiosensor+IPTG+Maltose. (E) Comparison of the repression (i.e.,reduction of relative expression) of reporter output upon expression ofthe SZF and SP biosensors compared to that mediated by inducingexpression of the BCR-ABL1 ZFP alone. Three top points are values fromSZF biosensors. Three bottom points are values from SP biosensors. (F)Tradeoff between level of expression of the SP biosensor (IPTG dose)with both repression (-maltose, bottom curve) and alleviation (+100 mMmaltose, top curve) of expression from the Go66 reporter. (G) Responseof reporter output to various extracellular concentrations of maltose,under two levels of biosensor expression (IPTG doses). Top horizontalbars indicate the 0 mM maltose case for 10 μM IPTG. Bottom horizontalboars indicated the 0 mM maltose case for 30 μM IPTG, with the width ofeach bar indicating one standard deviation. Top curves indicate 10 μMIPTG for increasing concentration of maltose: 0.1, 1, 10, or 100 mM.Bottom curves indicate 30 μM IPTG for increasing concentration ofmaltose: 0.1, 1, 10, or 100 mM. (H) The impact of the W340A mutation(SP: top curve: SP(W340A): bottom curve), which is reported to diminishmaltose binding⁷², on biosensor performance was evaluated using the Go92reporter and analyses paralleling those used in panel G. Horizontal barscorrespond to the indicated biosensor with 0 mM maltose, with the widthof each bar indicating one standard deviation. Microplate data werecollected over 7 sequential time points, spanning ˜1.5 h ofmid-exponential phase growth, and averaged. Relative expression wasutilized in order to implicitly correct for any minor effects that IPTGor maltose many confer on GFP/OD₆₀₀ in a manner that is unrelated toexpression of the ZFP or biosensor (see Materials and Methods fordetails). All data points represent mean values calculated from twoindependent experiments, each run in biological triplicate, and errorbars represent one standard deviation (**p≤0.01, ***p≤0.001).

FIG. 5. Contributions of biosensor biophysical properties to biosensorperformance. At top, the illustration summarizes the biosensor designspace explorations described in this figure, using the SP biosensor as areference case. SP-Zif268 incorporates the tighter binding Zif268 ZFP inplace of BCR-ABL1. In SP-mC and mC-SP, mCherry was fused to theC-terminus or N-terminus of the SP biosensor, respectively. Below,biosensor performance, when paired with the Go92 reporter, was evaluatedby inducing biosensor expression (30 μM IPTG) and evaluating alleviationof repression upon addition of maltose (100 mM). Microplate data werecollected over 7 sequential time points, spanning ˜1.5 h ofmid-exponential phase growth, and averaged. All data represent meanvalues calculated from two independent experiments, each run inbiological triplicate, and error bars represent one standard deviation(*p≤0.05, *** p≤0.001).

FIG. 6. Plasmid maps of representative plasmids used in this study.pAY242 is the pBAD (arabinose-inducible) vector expressing the BCR-ABL1zinc finger and mCherry (co-cistronically). This is a high copy plasmid(ColE1 origin) and contains the KanR resistance marker. pAY268 is arepresentative reporter plasmid with the Go3 promoter, driving theexpression of EGFP. This is a medium copy plasmid (pA15 origin) andcontains the Amp^(R) resistance marker. pAY419 drives expression of theSP biosensor (and mCherry, co-cistronically) from the pTrc2 promoter(which is IPTG-inducible). The site at which MBP is split via the BCR1insertion is indicated on this map. This is a high copy plasmid (ColE1origin) and contains the Kan^(R) resistance marker.

FIG. 7. Engineered promoter library details. Each BCR-ABL1-basedpromoter used in this study is listed and annotated as per the key attop. All Zif268 promoters (not listed) are identical in every way totheir BCR-ABL1 counterparts except that the Zif268 binding site(GCGTGGGCG) replaces each instance of the BCR-ABL1 binding site.

FIG. 8. Specific fluorescence variation across the promoter library. (A)The impact of BCR-ABL1 expression on GFP reporter output was evaluatedusing a library of promoters bearing BCR-ABL1 sites at various locationsin the promoter. The specific fluorescence (GFP fluorescence per OD₆₀₀)was measured for each promoter in the library in the absence of BCR-ABL1and normalized to that of the No Sites control promoter. (B) Comparisonof specific fluorescence without BCR-ABL1 to BCR-ABL1-mediatedrepressibility (relative expression); note that each quantity wasnormalized to value associated with the No Sites control promoter.Relative expression is defined as the ratio of GFP/OD₆₀₀ (for any givenpromoter) of the induced case relative to that of the uninduced case,divided by this same ratio for the No Sites promoter (a full descriptionand rationale can be found in the Materials and Methods section of themain manuscript). All experiments were run in biological triplicate, anderror bars indicate one standard deviation.

FIG. 9. Comparison of flow cytometry and microplate assay-basedquantification of BCR-ABL1-mediated repressibility. Select promoterconstructs were analyzed by both methods. Close association of eachpoint with the diagonal line (y=x) drawn as a visual guide indicatesagreement between the two methods of quantifying relative expression,with the possible exception of Go19, which was the least repressiblepromoter analyzed. All samples were normalized to the No Sites controlpromoter. Experiments were conducted in biological triplicate, and errorbars indicate one standard deviation.

FIG. 10. Feature selection for BCR-ABL1-mediated repression. (A)Shorthand names and descriptions of the 17 features chosen to describethe promoter library. (B) Partial least squares repression (PLSR)coefficients associated with each feature (bars) are plotted along withcorresponding standard deviations (error bars, which indicate the erroraround the mean coefficient value obtained by iterative permutation;this mean value was 0 in all cases, and thus error bars are plotted asdeviations from 0). (C) PLSR coefficients associated with each featurewere normalized by dividing each coefficient by its associated standarddeviation, as obtained by iterative permutation (see Materials andMethods). Features with a large normalized coefficient value are mostimportant. (D) Average mean squared errors obtained when each featurewas permuted during the Random Forest analysis (see Materials andMethods). Features associated with a high mean squared error (whenpermuted) are more important. Feature numbers correspond to those listedin FIG. 3A in the main text.

FIG. 11. Analysis of SP biosensor performance at the individual celllevel. Cells containing the SP biosensor and the Go92 reporter weregrown and induced in the same manner as was used for microplate readeranalysis, and then these cells were analyzed by flow cytometry. Cellswere gated using forward and side scatter to exclude debris. Thehistogram represents cells that contain neither the biosensor nor thereporter plasmids. The remaining histograms represent cells expressingthe SP biosensor and the Go92 reporter, cultured under the mediumconditions indicated.

FIG. 12. The effect of maltose on the BCR-ABL1 zinc finger'srepressibility over a range of IPTG induction levels. The BCR-ABL1 zincfinger was expressed with the pTrc2 promoter in combination with theGo66 reporter. Compare to biosensor performance in FIG. 4F. Note thatthe differences in repressibility between pTrc-BCR-ABL1 (shown here) andpBAD-BCR-ABL1 (shown in FIG. 1, FIGS. 2, 8, and 9) may be attributed todifferent levels of expression of this ZFP from the aforementionedpromoters. Given the potential for catabolite repression of the pBADpromoter in the presence of maltose, pTrc-BCR-ABL1 was constructed toenable testing the effect of maltose on BCR-ABL1-mediated repression ofthe reporters. All data represent mean values calculated from twoindependent experiments, each run in biological triplicate, and errorbars represent one standard deviation.

FIG. 13. Impact of 100 mM maltose and IPTG on cell growth. (A, B) Growthcurves were collected for cells transformed with either the SPbiosensor+the No Sites reporter (A) or the SP biosensor+the Go66reporter (B). Experiments were conducted in biological triplicate, anderror bars indicate one standard deviation.

FIG. 14. Fold induction and alleviation calculated using metricspreviously applied to natural biosensors. Using the fold inductionmethods described by Rogers et al. (2015), fold repression wascalculated by dividing the (maximum, uninduced background subtractedGFP/OD₆₀₀) by the (induced, background subtracted GFP/OD₆₀₀, repressedfluorescence). Fold alleviation was similarly calculated by dividing the(maximum, background subtracted GFP/OD₆₀₀, maltose-inducedfluorescence), by the (induced, background subtracted GFP/OD₆₀₀,repressed fluorescence). GFP/OD₆₀₀ values were calculated 10 hours afterinduction. Each range indicated is one standard deviation, in whicherror was propagated according to the division rule.

FIG. 15. Overview of the BERDI method for generating novelmetabolite-responsive transcription factor biosensors. (A) Librarygeneration—The donor plasmid containing the gene of interest, thetransposon, and the transposase enzymes create a library of randominsertions. (B) Cloning of the transposed gene. Gene containing theinserted transposon is isolated, and cloned into a similarly digestedexpression plasmid. (C) Exchanging the transposon for the ZFP. Thetransposon is replaced with ZFP and are transformed into cellscontaining the reporter GFP plasmid. (D) Cartoon of potential enrichmentstrategy for metabolite responsive biosensors using FACS. See materialsand methods for experimental details.

FIG. 16. Analysis of naïve library diversity. (A) Initial evaluation ofdistribution of biosensor diversity via gel electrophoresis comparingthe naïve library of biosensors, to the same sample now treated with arestriction enzyme unique to the ZFP (BamHI). Lane 1, full lengthbiosensors (1455 bp). Lane 2, digested biosensors. Lane 3, DNA ladderwith the corresponding bp values listed. (B) Intensity trace of bothlanes from panel A created in ImageJ. Position zero maps to the space inthe gel below any visible DNA, taken to be the background intensity. (C)Experimentally observed ZFP insertion positions into MBP. Insertionsfound via NGS and/or via colony (Sanger) sequencing are displayed.

FIG. 17. Isolation of functional biosensors by screening. (A) Cartoondepicting the overall biosensor enrichment strategy applied. Briefly,three rounds of FACS were performed, each time sorting the inducedpopulation using a gate encompassing no more than 1% of the “ON”(uninduced) population. One subsequent round of sorting was done toisolate only repressors that were reversible. Cells were then plated,and clonally evaluated for maltose responsiveness. Successful biosensorswere sequenced to determine the insertions position. (B) Crystalstructure of MBP is shown (PDB #1ANF)(Quiocho et al., 1997), with theinsertional positions (in amino acid number) of each biosensor labeled.A cluster of spheres represents the ligand, maltose (space-fillingmodel). (C) Flow cytometry of the reference biosensor compared to thethree new biosensors. Biosensor production was induced with 30 μM IPTGand maltose was added at 100 mM. The insertional position (in amino acidnumber), and whether the ZFP is a single, or double insertion, is listedin the top left corner of each plot. Plots represent a minimum of 10,000cells in each condition and are representative of multiple independentexperiments.

FIG. 18. Impact of biosensor expression level on performance. Responseof reporter output to the addition of IPTG and IPTG along with maltosemeasured by flow cytometry. The maltose concentration used here was 100mM. The vertical line at 10³ GFP fluorescence units is a visual aid tofacilitate comparison across the conditions. Plots represent multipleindependent experiments.

FIG. 19. Impact of linker tuning on biosensor performance. (A)Comparison of the effect of amino acid linkers between referencebiosensor and its transposon-created counterpart on repressibility andalleviation with maltose. (B) Impact of varying linker lengths for the335P biosensor. Biosensors were induced with 30 μM IPTG and maltose wasadded at 100 mM final concentration. (C) Mean fluorescence intensity ofthe four linker variants of the 335P biosensor measured via flowcytometry. Biosensors were induced with 30 μM IPTG and maltose was addedat 100 mM final concentration. Samples were run in biologicaltriplicate, and error bars represent one standard deviation. (*p≤0.001from a two-tailed students t-test). All data are representative ofmultiple independent experiments.

FIG. 20. Transposon key features and scar options. (A) Cartoonrepresentation of the transposon. The transposon is digested out of itsstorage vector pAY438 using the BgIII restriction enzymes. This leavesthe minimal 3′ “A” that is essential for MuA mediated strand transferduring the transposition reaction. All bases outside of this “A” getcleaved from the transposon following transposition. Highlighted baseswill remain in the gene of interest after the transposon has beenexcised out. The NotI site is embedded in the MuA recognition site. TheMuA recognition sites are show in boxes. The CamR and SacB ORFs withtheir own constitutive promoters are contained within the MuArecognition sequences. (B) The transposon will insert irrespective offrame: therefore, for each of the three frames, we describe thepotential bases that need to be added to the ZFP to ensure that bothregions of MBP are in frame with the ZFP insertion. By adding a singlebase to the front of the ZFP for frame 2, all the linkers will bealanines, except for the codon interrupted by transposon, and thiscannot become a STOP codon. Frames 1 and 3 yielded poor linker optionsafter the frame of the ZFP and MBP was preserved. Codons containing a“*” are controllable by varying the “X” base identity. FIG. 20Bdiscloses SEQ ID NOS 73-78, respectively, in order of appearance.

FIG. 21. Representative graphic of the primers used to prepare libraryfor deep sequencing. The zinc finger could potentially be anywhereinside MBP and is only drawn in the middle here for ease ofvisualization. All primers contained a variant of the common sequencesnecessary for the downstream amplification conducted by the nextgeneration sequencing core. Primers were spaced apart in order tocapture insertions in windows of 300 bp, as the NGS required amplicons<500 bp in length. The top cartoon describes the primers used for theforward facing ZFP, whereas the bottom cartoon described the reactionsthat were done to account for possible reverse ZFP insertions due to thepalindromic nature of the transposon recognition sequence. Please seeTable 2 for description of the 8 PCRs.

FIG. 22. Flow Chart of NGS Analysis Pipeline. This diagram summarizesthe logic of the custom NGS data analysis program written for thisstudy. Functions were created to conduct each task are outlined in theboxes. The boxes indicate steps where a Needleman-Wunsch (N-W) alignmentis performed or indicate post-processing steps that only require theNeedleman-Wunsch output alignment file of the insert aligned against thetemplate. Phred quality scores are a measure of the quality ofidentification of a particular nucleotide during the NGS process; ascore of 20 corresponds to 99% confidence in the identity of that base.

FIG. 23. 270A double ZFP versus 270A single ZFP. The effects the numberof zinc finger inserts into MBP at position 270A on biosensorperformance were evaluated using flow cytometry for this construct. IPTGwas used to induce biosensor repression, and the maltose additionrelieved repression. The plots shown use varying concentrations of IPTGand are grouped by the number of zinc fingers present with (A) two zincfinger insertions, (B) one zinc finger insertion.

DETAILED DESCRIPTION

The present invention is described herein using several definitions, asset forth below and throughout the application.

Definitions

Unless otherwise specified or indicated by context, the terms “a”, “an”,and “the” mean “one or more.” For example, “a component” should beinterpreted to mean “one or more components.”

As used herein, “about,” “approximately,” “substantially,” and“significantly” will be understood by persons of ordinary skill in theart and will vary to some extent on the context in which they are used.If there are uses of these terms which are not clear to persons ofordinary skill in the art given the context in which they are used,“about” and “approximately” will mean plus or minus ≤10% of theparticular term and “substantially” and “significantly” will mean plusor minus >10% of the particular term.

As used herein, the terms “include” and “including” have the samemeaning as the terms “comprise” and “comprising” in that these latterterms are “open” transitional terms that do not limit claims only to therecited elements succeeding these transitional terms. The term“consisting of,” while encompassed by the term “comprising,” should beinterpreted as a “closed” transitional term that limits claims only tothe recited elements succeeding this transitional term. The term“consisting essentially of,” while encompassed by the term “comprising,”should be interpreted as a “partially closed” transitional term whichpermits additional elements succeeding this transitional term, but onlyif those additional elements do not materially affect the basic andnovel characteristics of the claim.

The disclosed technology relates to “biosensors.” As disclosed herein, a“biosensor” is a molecule or a system of molecules that can be used tobind to a ligand and provide a detectable response based on binding theligand. In some cases, “biosensors” may be referred to as “molecularswitches.” Biosensors and molecular switches are disclosed in the art.(See, e.g., Ostermeier, Protein Eng. Des. Sel. 2005 August;18(8):359-64; Wright et al., Curr. Opin. Chem. Biol. 2007 June;11(3):342-6; Roberts, Chem. Biol. 2004 November; 11(11): 1475-6; andU.S. Pat. Nos. 8,771,679; 8,679,753; and 8,338,138; the contents ofwhich are incorporated herein by reference in their entireties).Biosensors and molecular switches have been utilized in recombinantmicroorganisms. (See, e.g., Rogers et al., Curr. Opin. Biotechnol. 2016Mar. 18; 42:84-91; and U.S. Published Application Nos. 2010/0242345 and2013/0059295; the contents of which are incorporated herein by referencein their entireties). As indicated, many results have been publisheddescribing the utility of using naturally occurring biosensors for novelpurposes, and applications include both high throughput screening andfeedback-mediated enhanced production of various products viabiosynthetic pathways. However, to the present inventors' knowledge, noone has published a bottom-up and generalizable strategy for convertingmetabolite-binding proteins into metabolite-responsive biosensors.

As used herein, the term “metabolite-binding protein” may be usedinterchangeably with the term “ligand-binding protein.” As contemplatedherein, a “ligand-binding protein” may include any protein that binds toa ligand. For example, a ligand-binding protein may include a receptorfor a ligand. A ligand-binding protein may include an enzyme, and wherethe ligand-binding protein is an enzyme, the substrate for the enzymemay corresponds to the ligand as contemplated herein. As such, the term“ligand” may be used interchangeably herein with the term “substrate.” Aligand-binding protein may include a periplasmic binding protein thatbinds a ligand or substrate. A ligand-binding protein may include atransporter that binds a ligand or substrate.

The systems, components, and methods disclosed herein may be utilizedfor sensing a ligand or a substrate or a metabolite in a cell or areaction mixture. The disclosed systems, components, and methodstypically include and/or utilize a fusion protein comprising aligand-binding protein and a DNA-binding protein. The fusion protein ofthe disclosed systems and methods may otherwise be referred to as a“biosensor” as contemplated herein. The fusion proteins or biosensorsdisclosed herein bind the ligand of the ligand-binding protein andmodulate expression of a reporter gene operably linked to a promoterthat is engineered to include specific binding sites for the DNA-bindingprotein. The difference in expression of the reporter gene in thepresence of the ligand versus expression of the reporter gene in theabsence of the ligand can be correlated to the concentration of theligand in a reaction mixture. As such, in some embodiments, thedisclosed fusion proteins or biosensors may be referred to asmetabolite-responsive transcription factors.

In some embodiments, the fusion protein or biosensor binds to thepromoter that is engineered to include specific binding sites for theDNA-binding protein with an affinity (K_(d1)) in the absence of theligand. When the ligand is present, the fusion protein or biosensorbinds the ligand, and then the fusion protein has a second bindingaffinity (K_(d2)) for the promoter in the presence of the ligand. Forexample, the fusion protein or biosensor may bind the ligand and undergoa conformation change that alters the binding affinity of the fusionprotein or biosensor for the promoter. In some embodiments of thedisclosed systems K_(d1)<K_(d2), and in other embodiments of thedisclosed systems K_(d1)>K_(d2). The difference in affinities in thepresence and absence of the ligand may be based on a conformationalchange that the fusion protein exhibits in the presence of the ligandversus the absence of the ligand. The fusion protein or biosensor maymodulate expression of the report gene based on whether the fusionprotein or biosensor is bound to the promoter or the fusion protein orbiosensor is not bound to the promoter, and the modulation may becorrelated with the concentration of the ligand in the system.

As used herein, “modulating expression” may include “repressingexpression” and/or “inhibiting expression,” and “modulating expressionmay include “de-repressing expression” and/or “activating expression.”As such, in some embodiments, when the fusion protein or biosensor isnot bound to a ligand, the fusion protein or biosensor may repressexpression and/or inhibit expression from a promoter that is engineeredto include specific binding sites for the DNA-binding protein, and whenthe fusion protein or biosensor is bound to the ligand the fusionprotein may de-repress and/or activate expression from the promoter.De-repression and/or activation of the expression of the reporter genethen can be correlated with the presence of the ligand. In otherembodiments, when the fusion protein or biosensor is bound to a ligand,the fusion protein or biosensor may repress expression and/or inhibitexpression from the promoter that is engineered to include specificbinding sites for the DNA-binding protein, and when the fusion proteinor biosensor is not bound to the ligand the fusion protein or biosensormay de-repress expression and/or activate expression from the promoter.A decrease in expression of the reporter gene then can be correlatedwith the presence of the ligand.

In some embodiments, when the fusion protein or biosensor is bound tothe promoter engineered to include specific binding sites for theDNA-binding protein, the fusion protein may repress and/or inhibitexpression of the report gene. Then, in the presence of the ligand forthe ligand binding protein, the fusion protein or biosensor may bind theligand and de-repress and/or activate expression of the reporter genethat is operably linked to the promoter. For example, in the presence ofthe ligand the fusion protein or biosensor may no longer bind to thepromoter or may bind to the promoter with a lower affinity such thatexpression of the reporter gene is de-repressed and/or activated (i.e.,K_(d1)<K_(d2)). In the absence of the ligand, the fusion protein orbiosensor may undergo a conformational shift whereby the fusion proteinor biosensor binds to the promoter or binds to the promoter with ahigher affinity than in the presence of the ligand. De-repression and/oractivation of the expression of the reporter gene then can be correlatedwith the presence of the ligand.

In other embodiments, when the fusion protein or biosensor is bound tothe promoter engineered to include specific binding sites for theDNA-binding protein, the fusion protein or biosensor may activateexpression of the reporter gene. Then, in the presence of the ligand forthe ligand binding protein, the fusion protein or biosensor may bind theligand and no longer activate expression of the reporter gene or mayrepress or inhibit expression of the reporter gene, effectivelydecreasing expression of the reporter gene. For example, in the presenceof the ligand the fusion protein or biosensor may bind to the promoterwith a higher affinity than in the absence of the ligand (i.e.,K_(d1)>K_(d2)) and activate expression. In the absence of the ligand,the fusion protein or biosensor may undergo a conformational shift andmay no longer bind the promoter or may bind the promoter with a loweraffinity than in the presence of the ligand and no longer activateexpression of the reporter gene. A decrease in expression of thereporter gene then can be correlated with the presence of the ligand.

The disclosed biosensors, systems, and methods may be utilized and/orperformed using any suitable cell. Suitable cells may includeprokaryotic cells and eukaryotic cells.

Reference is made herein to nucleic acid and nucleic acid sequences. Theterms “nucleic acid” and “nucleic acid sequence” refer to a nucleotide,oligonucleotide, polynucleotide (which terms may be usedinterchangeably), or any fragment thereof. These phrases also refer toDNA or RNA of genomic or synthetic origin (which may be single-strandedor double-stranded and may represent the sense or the antisense strand).

Reference also is made herein to peptides, polypeptides, proteins andcompositions comprising peptides, polypeptides, and proteins. As usedherein, a polypeptide and/or protein is defined as a polymer of aminoacids, typically of length≥100 amino acids (Garrett & Grisham,Biochemistry, 2^(nd) edition, 1999, Brooks/Cole, 110). A peptide isdefined as a short polymer of amino acids, of a length typically of 20or less amino acids, and more typically of a length of 12 or less aminoacids (Garrett & Grisham, Biochemistry, 2^(nd) edition, 1999,Brooks/Cole, 110).

As disclosed herein, exemplary peptides, polypeptides, proteins maycomprise, consist essentially of, or consist of any reference amino acidsequence disclosed herein, or variants of the peptides, polypeptides,and proteins may comprise, consist essentially of, or consist of anamino acid sequence having at least about 80%, 90%, 95%, 96%, 97%, 98%,or 99% sequence identity to any amino acid sequence disclosed herein.Variant peptides, polypeptides, and proteins may include peptides,polypeptides, and proteins having one or more amino acid substitutions,deletions, additions and/or amino acid insertions relative to areference peptide, polypeptide, or protein. Also disclosed are nucleicacid molecules that encode the disclosed peptides, polypeptides, andproteins (e.g., polynucleotides that encode any of the peptides,polypeptides, and proteins disclosed herein and variants thereof).

The term “amino acid,” includes but is not limited to amino acidscontained in the group consisting of alanine (Ala or A), cysteine (Cysor C), aspartic acid (Asp or D), glutamic acid (Glu or E), phenylalanine(Phe or F), glycine (Gly or G), histidine (His or H), isoleucine (Ile orI), lysine (Lys or K), leucine (Leu or L), methionine (Met or M),asparagine (Asn or N), proline (Pro or P), glutamine (Gln or Q),arginine (Arg or R), serine (Ser or S), threonine (Thr or T), valine(Val or V), tryptophan (Trp or W), and tyrosine (Tyr or Y) residues. Theterm “amino acid residue” also may include amino acid residues containedin the group consisting of homocysteine, 2-Aminoadipic acid,N-Ethylasparagine, 3-Aminoadipic acid, Hydroxylysine, β-alanine,β-Amino-propionic acid, allo-Hydroxylysine acid, 2-Aminobutyric acid,3-Hydroxyproline, 4-Aminobutyric acid, 4-Hydroxyproline, piperidinicacid, 6-Aminocaproic acid, Isodesmosine, 2-Aminoheptanoic acid,allo-Isoleucine, 2-Aminoisobutyric acid, N-Methylglycine, sarcosine,3-Aminoisobutyric acid, N-Methylisoleucine, 2-Aminopimelic acid,6-N-Methyllysine, 2,4-Diaminobutyric acid, N-Methylvaline, Desmosine,Norvaline, 2,2′-Diaminopimelic acid, Norleucine, 2,3-Diaminopropionicacid, Ornithine, and N-Ethylglycine. Typically, the amide linkages ofthe peptides are formed from an amino group of the backbone of one aminoacid and a carboxyl group of the backbone of another amino acid.

The amino acid sequences contemplated herein may include conservativeamino acid substitutions relative to a reference amino acid sequence.For example, a variant peptides, polypeptides, and proteins ascontemplated herein may include conservative amino acid substitutionsrelative to an amino acid sequence of a reference peptide, polypeptide,or protein. “Conservative amino acid substitutions” are thosesubstitutions that are predicted to interfere least with the propertiesof the reference peptide, polypeptide, or protein. In other words,conservative amino acid substitutions substantially conserve thestructure and the function of the reference peptide, polypeptide, orprotein. The following table provides a list of exemplary conservativeamino acid substitutions.

Table of Conservative Amino Acid Substitutions Original ConservativeResidue Substitution Ala Gly, Ser Arg His, Lys Asn Asp, Gln, His AspAsn, Glu Cys Ala, Ser Gln Asn, Glu, His Glu Asp, Gln, His Gly Ala HisAsn, Arg, Gln, Glu Ile Leu, Val Leu Ile, Val Lys Arg, Gln, Glu Met Leu,Ile Phe His, Met, Leu, Trp, Tyr Ser Cys, Thr Thr Ser, Val Trp Phe, TyrTyr His, Phe, Trp Val Ile, Leu, Thr

“Non-conservative amino acid substitutions” are those substitutions thatare predicted to interfere most with the properties of the referencepeptide, polypeptide, or protein. For example, a non-conservative aminoacid substitution might replace a basic amino acid at physiological pHsuch as Arg, His, or Lys, with a non-basic or acidic amino acid atphysiological pH such as Asp or Glu. A non-conservative amino acidsubstitution might replace a non-polar amino acid at physiological pHsuch as Ala, Gly, Ile, Leu, Phe, or Val, with a polar amino acid atphysiological pH such as Arg, Asp, Glu, His, or Lys.

The peptides, polypeptides, and proteins disclosed herein may bemodified to include non-amino acid moieties. Modifications may includebut are not limited to carboxylation (e.g., N-terminal carboxylation viaaddition of a di-carboxylic acid having 4-7 straight-chain or branchedcarbon atoms, such as glutaric acid, succinic acid, adipic acid, and4,4-dimethylglutaric acid), amidation (e.g., C-terminal amidation viaaddition of an amide or substituted amide such as alkylamide ordialkylamide), PEGylation (e.g., N-terminal or C-terminal PEGylation viaadditional of polyethylene glycol), acylation (e.g., O-acylation(esters), N-acylation (amides), S-acylation (thioesters)), acetylation(e.g., the addition of an acetyl group, either at the N-terminus of theprotein or at lysine residues), formylation lipoylation (e.g.,attachment of a lipoate, a C8 functional group), myristoylation (e.g.,attachment of myristate, a C14 saturated acid), palmitoylation (e.g.,attachment of palmitate, a C16 saturated acid), alkylation (e.g., theaddition of an alkyl group, such as an methyl at a lysine or arginineresidue), isoprenylation or prenylation (e.g., the addition of anisoprenoid group such as farnesol or geranylgeraniol), amidation atC-terminus, glycosylation (e.g., the addition of a glycosyl group toeither asparagine, hydroxylysine, serine, or threonine, resulting in aglycoprotein). Distinct from glycation, which is regarded as anonenzymatic attachment of sugars, polysialylation (e.g., the additionof polysialic acid), glypiation (e.g., glycosylphosphatidylinositol(GPI) anchor formation, hydroxylation, iodination (e.g., of thyroidhormones), and phosphorylation (e.g., the addition of a phosphate group,usually to serine, tyrosine, threonine or histidine).

Variants comprising deletions relative to a reference amino acidsequence or nucleotide sequence are contemplated herein. A “deletion”refers to a change in the amino acid or nucleotide sequence that resultsin the absence of one or more amino acid residues or nucleotidesrelative to a reference sequence. A deletion removes at least 1, 2, 3,4, 5, 10, 20, 50, 100, or 200 amino acids residues or nucleotides. Adeletion may include an internal deletion or a terminal deletion (e.g.,an N-terminal truncation or a C-terminal truncation or both of areference polypeptide or a 5′-terminal or 3′-terminal truncation or bothof a reference polynucleotide).

Variants comprising a fragment of a reference amino acid sequence ornucleotide sequence are contemplated herein. A “fragment” is a portionof an amino acid sequence or a nucleotide sequence which is identical insequence to but shorter in length than the reference sequence. Afragment may comprise up to the entire length of the reference sequence,minus at least one nucleotide/amino acid residue. For example, afragment may comprise from 5 to 1000 contiguous nucleotides orcontiguous amino acid residues of a reference polynucleotide orreference polypeptide, respectively. In some embodiments, a fragment maycomprise at least 5, 10, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,40, 50, 60, 70, 80, 90, 100, 150, 250, or 500 contiguous nucleotides orcontiguous amino acid residues of a reference polynucleotide orreference polypeptide, respectively. Fragments may be preferentiallyselected from certain regions of a molecule, for example the N-terminalregion and/or the C-terminal region of a polypeptide or the 5′-terminalregion and/or the 3′ terminal region of a polynucleotide. The term “atleast a fragment” encompasses the full length polynucleotide or fulllength polypeptide.

Variants comprising insertions or additions relative to a referencesequence are contemplated herein. The words “insertion” and “addition”refer to changes in an amino acid or nucleotide sequence resulting inthe addition of one or more amino acid residues or nucleotides. Aninsertion or addition may refer to 1, 2, 3, 4, 5, 10, 20, 30, 40, 50,60, 70, 80, 90, 100, 150, or 200 amino acid residues or nucleotides.

Fusion proteins and fusion polynucleotides also are contemplated herein.A “fusion protein” refers to a protein formed by the fusion of at leastone peptide, polypeptide, protein or variant thereof as disclosed hereinto at least one molecule of a heterologous peptide, polypeptide, proteinor variant thereof. The heterologous protein(s) may be fused at theN-terminus, the C-terminus, or both termini. A fusion protein comprisesat least a fragment or variant of the heterologous protein(s) that arefused with one another, preferably by genetic fusion (i.e., the fusionprotein is generated by translation of a nucleic acid in which apolynucleotide encoding all or a portion of a first heterologous proteinis joined in-frame with a polynucleotide encoding all or a portion of asecond heterologous protein). The heterologous protein(s), once part ofthe fusion protein, may each be referred to herein as a “portion”,“region” or “moiety” of the fusion protein. For example, where thefusion protein comprises at least a portion of a ligand binding proteinand at least a portion of a DNA-binding portion, the portions of thefusion may be referred to as “a ligand binding portion” and “aDNA-binding portion,” respectively.

A fusion polynucleotide refers to the fusion of the nucleotide sequenceof a first polynucleotide to the nucleotide sequence of a secondheterologous polynucleotide (e.g., the 3′ end of a first polynucleotideto a 5′ end of the second polynucleotide). Where the first and secondpolynucleotides encode proteins, the fusion may be such that the encodedproteins are in-frame and results in a fusion protein. The first andsecond polynucleotide may be fused such that the first and secondpolynucleotide are operably linked (e.g., as a promoter and a geneexpressed by the promoter as discussed below).

“Homology” refers to sequence similarity or, interchangeably, sequenceidentity, between two or more polypeptide sequences or polynucleotidesequences. Homology, sequence similarity, and percentage sequenceidentity may be determined using methods in the art and describedherein.

The phrases “percent identity” and “% identity,” as applied topolypeptide sequences, refer to the percentage of residue matchesbetween at least two polypeptide sequences aligned using a standardizedalgorithm. Methods of polypeptide sequence alignment are well-known.Some alignment methods take into account conservative amino acidsubstitutions. Such conservative substitutions, explained in more detailabove, generally preserve the charge and hydrophobicity at the site ofsubstitution, thus preserving the structure (and therefore function) ofthe polypeptide. Percent identity for amino acid sequences may bedetermined as understood in the art. (See, e.g., U.S. Pat. No.7,396,664, which is incorporated herein by reference in its entirety). Asuite of commonly used and freely available sequence comparisonalgorithms is provided by the National Center for BiotechnologyInformation (NCBI) Basic Local Alignment Search Tool (BLAST) (Altschul,S. F. et al. (1990) J. Mol. Biol. 215:403 410), which is available fromseveral sources, including the NCBI, Bethesda, Md., at its website. TheBLAST software suite includes various sequence analysis programsincluding “blastp,” that is used to align a known amino acid sequencewith other amino acids sequences from a variety of databases.

Percent identity may be measured over the length of an entire definedpolypeptide sequence or may be measured over a shorter length, forexample, over the length of a fragment taken from a larger, definedpolypeptide sequence, for instance, a fragment of at least 15, at least20, at least 30, at least 40, at least 50, at least 70 or at least 150contiguous residues. Such lengths are exemplary only, and it isunderstood that any fragment length may be used to describe a lengthover which percentage identity may be measured.

A “variant” of a particular polypeptide sequence may be defined as apolypeptide sequence having at least 50% sequence identity to theparticular polypeptide sequence over a certain length of one of thepolypeptide sequences using blastp with the “BLAST 2 Sequences” toolavailable at the National Center for Biotechnology Information'swebsite. (See Tatiana A. Tatusova, Thomas L. Madden (1999), “Blast 2sequences—a new tool for comparing protein and nucleotide sequences”,FEMS Microbiol Lett. 174:247-250). In some embodiments a variantpolypeptide may show, for example, at least 60%, at least 70%, at least80%, at least 90%, at least 91%, at least 92%, at least 93%, at least94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least99% or greater sequence identity over a certain defined length relativeto a reference polypeptide.

A variant polypeptide may have substantially the same functionalactivity as a reference polypeptide. For example, a variant polypeptidemay exhibit or more biological activities associated with binding aligand and/or binding DNA at a specific binding site.

The terms “percent identity” and “% identity,” as applied topolynucleotide sequences, refer to the percentage of residue matchesbetween at least two polynucleotide sequences aligned using astandardized algorithm. Such an algorithm may insert, in a standardizedand reproducible way, gaps in the sequences being compared in order tooptimize alignment between two sequences, and therefore achieve a moremeaningful comparison of the two sequences. Percent identity for anucleic acid sequence may be determined as understood in the art. (See,e.g., U.S. Pat. No. 7,396,664, which is incorporated herein by referencein its entirety). A suite of commonly used and freely available sequencecomparison algorithms is provided by the National Center forBiotechnology Information (NCBI) Basic Local Alignment Search Tool(BLAST) (Altschul, S. F. et al. (1990) J. Mol. Biol. 215:403 410), whichis available from several sources, including the NCBI, Bethesda, Md., atits website. The BLAST software suite includes various sequence analysisprograms including “blastn,” that is used to align a knownpolynucleotide sequence with other polynucleotide sequences from avariety of databases. Also available is a tool called “BLAST 2Sequences” that is used for direct pairwise comparison of two nucleotidesequences. “BLAST 2 Sequences” can be accessed and used interactively atthe NCBI website. The “BLAST 2 Sequences” tool can be used for bothblastn and blastp (discussed above).

Percent identity may be measured over the length of an entire definedpolynucleotide sequence or may be measured over a shorter length, forexample, over the length of a fragment taken from a larger, definedsequence, for instance, a fragment of at least 20, at least 30, at least40, at least 50, at least 70, at least 100, or at least 200 contiguousnucleotides. Such lengths are exemplary only, and it is understood thatany fragment length may be used to describe a length over whichpercentage identity may be measured.

A “full length” polynucleotide sequence is one containing at least atranslation initiation codon (e.g., methionine) followed by an openreading frame and a translation termination codon. A “full length”polynucleotide sequence encodes a “full length” polypeptide sequence.

A “variant,” “mutant,” or “derivative” of a particular nucleic acidsequence may be defined as a nucleic acid sequence having at least 50%sequence identity to the particular nucleic acid sequence over a certainlength of one of the nucleic acid sequences using blastn with the “BLAST2 Sequences” tool available at the National Center for BiotechnologyInformation's website. (See Tatiana A. Tatusova, Thomas L. Madden(1999), “Blast 2 sequences—a new tool for comparing protein andnucleotide sequences”, FEMS Microbiol Lett. 174:247-250). In someembodiments a variant polynucleotide may show, for example, at least60%, at least 70%, at least 80%, at least 90%, at least 91%, at least92%, at least 93%, at least 94%, at least 95%, at least 96%, at least97%, at least 98%, or at least 99% or greater sequence identity over acertain defined length relative to a reference polynucleotide.

Nucleic acid sequences that do not show a high degree of identity maynevertheless encode similar amino acid sequences due to the degeneracyof the genetic code. It is understood that changes in a nucleic acidsequence can be made using this degeneracy to produce multiple nucleicacid sequences that all encode substantially the same protein.

“Operably linked” refers to the situation in which a first nucleic acidsequence is placed in a functional relationship with a second nucleicacid sequence. For instance, a promoter is operably linked to a codingsequence if the promoter affects the transcription or expression of thecoding sequence. Operably linked DNA sequences may be in close proximityor contiguous and, where necessary to join two protein coding regions,in the same reading frame.

A “recombinant nucleic acid” is a sequence that is not naturallyoccurring or has a sequence that is made by an artificial combination oftwo or more otherwise separated segments of sequence. This artificialcombination is often accomplished by chemical synthesis or, morecommonly, by the artificial manipulation of isolated segments of nucleicacids, e.g., by genetic engineering techniques such as those describedin Sambrook, J. et al. (1989) Molecular Cloning: A Laboratory Manual,2^(nd) ed., vol. 1 3, Cold Spring Harbor Press, Plainview N.Y. The termrecombinant includes nucleic acids that have been altered solely byaddition, substitution, or deletion of a portion of the nucleic acid.Frequently, a recombinant nucleic acid may include a nucleic acidsequence operably linked to a promoter sequence. Such a recombinantnucleic acid may be part of a vector that is used, for example, totransform a cell.

“Transformation” describes a process by which exogenous DNA isintroduced into a recipient cell. Transformation may occur under naturalor artificial conditions according to various methods well known in theart, and may rely on any known method for the insertion of foreignnucleic acid sequences into a prokaryotic or eukaryotic host cell. Themethod for transformation is selected based on the type of host cellbeing transformed and may include, but is not limited to, bacteriophageor viral infection, electroporation, heat shock, lipofection, andparticle bombardment. The term “transformed cells” includes stablytransformed cells in which the inserted DNA is capable of replicationeither as an autonomously replicating plasmid or as part of the hostchromosome, as well as transiently transformed cells which express theinserted DNA or RNA for limited periods of time.

A “composition comprising a given polypeptide” and a “compositioncomprising a given polynucleotide” refer broadly to any compositioncontaining the given polynucleotide or amino acid sequence. Thecomposition may comprise a dry formulation or an aqueous solution. Thecompositions may be stored in any suitable form including, but notlimited to, freeze-dried form and may be associated with a stabilizingagent such as a carbohydrate. The compositions may be aqueous solutioncontaining salts (e.g., NaCl), detergents (e.g., sodium dodecyl sulfate;SDS), and other components.

“Substantially isolated or purified” nucleic acid or amino acidsequences are contemplated herein. The term “substantially isolated orpurified” refers to nucleic acid or amino acid sequences that areremoved from their natural environment, and are at least 60% free,preferably at least 75% free, and more preferably at least 90% free,even more preferably at least 95% free from other components with whichthey are naturally associated.

ILLUSTRATIVE EMBODIMENTS

The disclosed subject matter relates to metabolite-responsivetranscription regulator biosensors. The following embodiments areillustrative and do not limit the scope of the claimed subject matter.

The disclosed subject matter may include systems and methods thatcomprise or utilize a biosensor. In some embodiments, the disclosedsystems and methods comprises or utilize: (a) a fusion proteincomprising a ligand-binding protein (LBP) and a DNA-binding protein(DBP) or portions or fragments thereof as described herein, the fusionprotein comprising an amino acid sequence represented as: (N-terminalportion of the LBP)-(DBP)-(C-terminal portion of the LBP); and (b) apromoter which can be operably linked to a reporter gene. In thedisclosed systems and methods, the promoter typically includes at leastone heterologous binding site that is specific for the DBP and thefusion protein binds to the binding site and represses and/or inhibitstranscription of the reporter gene in a cell or a reaction mixture whenthe ligand for the LBP is not present in the cell or the reactionmixture.

In the disclosed systems and methods, the fusion protein may bind to thepromoter with an affinity (K_(d1)) in the absence of the ligand. Whenthe ligand is present, the fusion protein preferably binds the ligandand the fusion protein then has a second binding affinity (K_(d2)) forthe promoter in the presence of the ligand (or the fusion protein nolonger binds the promoter). In some embodiments, the difference inaffinities in the presence and absence of the ligand may be based on aconformational change that the fusion protein exhibits in the presenceof the ligand versus the absence of the ligand.

Where K_(d1)<K_(d2), transcription of the reporter gene may bede-repressed or activated in the presence of the ligand. In someembodiments, de-repression or activation in the presence of the ligandis proportional to the concentration of the ligand in the cell or thereaction mixture. In this embodiment, preferably the fusion proteinbinds to the promoter with a relatively high K_(d1) in the absence ofthe ligand (e.g., with a K_(d1)<about 1 nM, 0.5 nM, 0.2 nM, 0.1 nM, 0.05nM, 0.02 nM, 0.01 nM or lower). In this embodiment, preferably thefusion protein binds to the promoter with a relatively low K_(d2) in thepresence of ligand (e.g., with a K_(d2)>about 0.02 nM, 0.05 nM, 0.1 nM,0.2 nM, 0.5 nM, 1 nM, 2 nM or higher). In this embodiment, preferablythe ratio K_(d2):K_(d1) is at least about 5, 10, 20, 50, 100, 500, 1000or more.

Where K_(d1)>K_(d2), transcription of the reporter gene may be activatedby the fusion protein when the fusion protein is bound to the ligand andexpression may be no longer activated, repressed, or inhibited in theabsence of the ligand, effectively decreasing expression in the absenceof the ligand. In this embodiment, preferably the fusion protein bindsto the promoter with a relatively high K_(d1) in the presence of theligand (e.g., with a K_(d1)<about 1 nM, 0.5 nM, 0.2 nM, 0.1 nM, 0.05 nM,0.02 nM, 0.01 nM or lower). In this embodiment, preferably the fusionprotein binds to the promoter with a relatively low K_(d2) in theabsence of ligand (e.g., with a K_(d2)>about 0.02 nM, 0.05 nM, 0.1 nM,0.2 nM, 0.5 nM, 1 nM, 2 nM or higher). In this embodiment, preferablythe ratio K_(d1):K_(d2) is at least about 5, 10, 20, 50, 100, 500, 1000or more.

In the disclosed systems and methods, the fusion protein includes atleast an N-terminal portion of the ligand-binding protein (LBP) fused atthe N-terminus to at least a portion of the DNA-binding protein (DBP).In some embodiments, the fusion protein comprises an amino acid sequenceof at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 contiguousamino acids from the N-terminus of the LBP fused at the N-terminus to atleast a portion of the DBP.

In the disclosed systems and methods, the fusion protein includes atleast a C-terminal portion of the ligand-binding protein (LBP)-fused atthe C-terminus to at least a portion of the DNA-binding protein (DBP).In some embodiments, the fusion protein comprises an amino acid sequenceof at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 contiguousamino acids from the C-terminus of the LBP fused at the C-terminus to atleast a portion of the DBP.

In the disclosed systems and methods, the ligand-binding protein (LBP)binds to a ligand. In some embodiments of the systems and methodsdisclosed herein, the ligand is a cellular metabolite and the fusionprotein and systems and methods disclosed herein may be utilized todetect and measure cellular metabolism. In some embodiments, the LBP ismaltose binding protein and the ligand is maltose.

In the disclosed systems and methods, the fusion protein includes atleast a portion of a DNA-binding protein (DBP). In some embodiments, theDBP comprises one or more DNA-binding domains selected from the groupconsisting of a zinc-finger protein (ZFP) DNA-binding domain, atranscription activator-like effector (TALE) DNA-binding domain, and aclustered regularly interspaced short palindromic repeat (CRISPR)DNA-binding domain. Suitable zinc-finger proteins may include, but arenot limited to BCR-ABL1.

The disclosed systems and methods typically include or utilize apolynucleotide comprising a promoter, which may be operably linked to areporter gene. The polynucleotide comprising a promoter, which may beoperably linked to a reporter gene may be referred to as a reportercassette. Suitable promoters include, but are not limited to,prokaryotic promoters. The promoter of the disclosed systems and methodstypically is modified by inserting into the promoter a heterologoussequence that comprises one or more binding sites for the DNA-bindingprotein (DBP). In some embodiments, the promoter includes two, three, ormore binding sites for the DBP. The binding sites are inserted in thepromoter at positions such that when the DBP binds to the binding sites,the expression of a reporter gene that is operably linked to thepromoter is repressed. In some embodiments, the binding sites arelocated at one or more positions in the promoter selected from: (i)between the −10 box (TATA box) and the −35 box (GC-rich region); (ii)adjacent to the −10 box or within 5 nucleotides of the −10 box; and/or(iii) adjacent to the −35 box or within 5 nucleotides of the −35 box.

The disclosed systems and methods may include or utilize a reporter genethat is operably linked to the promoter of the systems and methods, forexample as part of a reporter cassette. Suitable reporter genes mayprovide a detectable signal when expressed (e.g., fluorescence of GFP)and/or may provide a selectable marker when expressed (e.g., a markerfor anti-biotic resistance such as β-lactamase).

Also disclosed herein are methods for making and selecting componentsfor use in the disclosed systems including methods for making andselecting biosensors or fusion proteins as discussed herein In someembodiments, the disclosed methods may be performed to prepare andselect a fusion proteins comprising a ligand-binding protein (LBP) and aDNA-binding protein (DBP) or portions or fragments thereof as describedherein, the fusion protein comprising an amino acid sequence representedas: (N-terminal portion of the LBP)-(DBP)-(C-terminal portion of theLBP). A library of fusion proteins may be prepared by inserting the DBPrandomly into the LBP, for example, by performing a recombinant DNAmethod such as transposon-mediated recombination. One or more fusionproteins of the library then may be tested and selected for use asbiosensors in the systems and methods disclosed herein. For example, thefusion proteins of the library may be tested for repressingtranscription from a promoter that includes at least one heterologousbinding site that is specific for the DBP, where the fusion proteinbinds to the binding site of the promoter and represses and/or inhibitstranscription from the promoter when the ligand for the LBP is notpresent.

Applications for the disclosed technology include, but are not limitedto: (i) novel sensing in which the novel metabolite-responsivebiosensors enable real time monitoring of small molecules in livingcells; (ii) high-throughput screening in which the novelmetabolite-responsive biosensors can be used to rapidly screening verylarge <10⁸ genetic libraries for high-producing strains; and (iii)dynamic feedback control in which the novel metabolite-responsivebiosensors that regulate transcription can enable the engineering offeedback control to optimize production of product molecules via naturaland/or engineered biosynthetic pathways.

Advantages of the disclosed technology include, but are not limited to:(i) the disclosed technology enables the use of biosensors to monitorthe many metabolites not recognized by natural biosensors; (ii) thedisclosed technology is generalizable in that the disclosed technologyenables leveraging the wealth of naturally occurring metabolite-bindingproteins into metabolite responsive transcriptional regulator proteins;(iii) the disclosed technology is broadly applicable because it utilizesmodular DNA binding proteins, such as zinc finger proteins, such thatnovel biosensors can be easily programmed to regulate specific targetgenes; and (iv) the disclosed technology provides a library of zincfinger-responsive promoters, which we have built and characterized, thatexhibit a range of response profiles, and as such, a user canpredictably implement a desired biosensor-regulated function by pairingan engineered biosensor with a desired promoter design based upon theprovided library.

In one embodiment, the present inventors developed a generalizablestrategy for engineering novel metabolite-responsive transcriptionalregulators. The inventors explored several strategies for converting aligand-binding protein into a functioning biosensor, appliedquantitative analysis to identify rules for designingbiosensor-regulated promoters, and quantitatively characterized thesenovel biological parts. The inventor's systematic investigation guidesthe engineering of customized metabolite-responsive biosensors.

ILLUSTRATIVE EMBODIMENTS

The following embodiments are illustrative and should not be interpretedto limit the scope of the claimed subject matter.

Embodiment 1

A system comprising: (a) a fusion protein that functions as a biosensor,the fusion protein comprising a ligand-binding protein (LBP) and aDNA-binding protein (DBP), the fusion protein comprising an amino acidsequence represented as: (N-terminal portion of theLBP)-(DBP)-(C-terminal portion of the LBP); and (b) a promoter which canbe operably linked to a reporter gene, wherein the promoter comprises atleast one heterologous binding site that is specific for the DBP and thefusion protein binds to the ligand and modulates expression from thepromoter.

Embodiment 2

The system of embodiment 1, wherein the fusion protein has a firstbinding affinity (K_(d1)) for the promoter in the absence of the ligand,and the fusion protein has a second binding affinity (K_(d2)) for thepromoter in the presence of the ligand, such that K_(d1)<K_(d2) andtranscription of the reporter gene is de-repressed or activated in thepresence of the ligand.

Embodiment 3

The system of embodiment 2, wherein de-repression or activation isproportional to concentration of the ligand in the system.

Embodiment 4

The system of embodiment 2 or 3, wherein K_(d1) is <about 1 nM, 0.5 nM,0.2 nM, 0.1 nM, 0.05 nM, 0.02 nM, 0.01 nM or lower.

Embodiment 5

The system of any of embodiments 2-4, wherein K_(d2) is >about 0.02 nM,0.05 nM, 0.1 nM, 0.2 nM, 0.5 nM, 1 nM, 2 nM or higher.

Embodiment 6

The system of any of embodiments 2-5, wherein the ratio K_(d2):K_(d1) isat least about 5, 10, 20, 50, 100, 500, 1000 or more.

Embodiment 7

The system of embodiment 1, wherein the fusion protein has a firstbinding affinity (K_(d1)) for the promoter in the absence of the ligand,and the fusion protein has a second binding affinity (K_(d2)) for thepromoter in the presence of the ligand, such that K_(d1)>K_(d2) andtranscription of the reporter gene is repressed or de-activated in thepresence of the ligand.

Embodiment 8

The system of embodiment 7, wherein repression or de-activation isproportional to concentration of the ligand in the system.

Embodiment 9

The system of embodiment 7 or 8, wherein K_(d2) is <about 1 nM, 0.5 nM,0.2 nM, 0.1 nM, 0.05 nM, 0.02 nM, 0.01 nM or lower.

Embodiment 10

The system of any of embodiments 7-9, wherein K_(d1) is >about 0.02 nM,0.05 nM, 0.1 nM, 0.2 nM, 0.5 nM, 1 nM, 2 nM or higher.

Embodiment 11

The system of any of embodiments 7-10, wherein the ratio K_(d1):K_(d2)is at least about 5, 10, 20, 50, 100, 500, 1000 or more.

Embodiment 12

The system of any of the foregoing embodiments, wherein the N-terminalportion of the LBP within the fusion protein comprises an amino acidsequence of at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100contiguous amino acids from the N-terminus of the LBP.

Embodiment 13

The system of any of the foregoing embodiments, wherein the C-terminalportion of the LBP within the fusion protein comprises an amino acidsequence of at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100contiguous amino acids from the C-terminus of the LBP.

Embodiment 14

The system of any of the foregoing embodiments, wherein the ligand is acellular metabolite.

Embodiment 15

The system of any of the foregoing embodiments, wherein the LBP ismaltose binding protein.

Embodiment 16

The system of any of the foregoing embodiments, wherein the DBPcomprises one or more DNA-binding domains selected from the groupconsisting of a zinc-finger protein (ZFP) DNA-binding domain, atranscription activator-like effector (TALE) DNA-binding domain, and aclustered regularly interspaced short palindromic repeat (CRISPR)DNA-binding domain.

Embodiment 17

The system of embodiment 16, wherein the ZFP is BCR-ABL1.

Embodiment 18

The system of any of the foregoing embodiments, wherein the promoter isa prokaryotic promoter.

Embodiment 19

The system of embodiment 18, wherein the promoter comprises two or morebinding sites for the DBP.

Embodiment 20

The system of embodiment 19, wherein the binding sites are located atone or more positions selected from: (i) between the −10 box (TATA box)and the −35 box (GC-rich region); (ii) adjacent to the −10 box (TATAbox) or within 5 nucleotides of the −10 box (TATA box); and (iii)adjacent to the −35 box (GC-rich region) or within 5 nucleotide of the−35 box (GC-rich region).

Embodiment 21

A method for preparing a fusion protein for use as a biosensor, thefusion protein comprising a ligand-binding protein (LBP) and aDNA-binding protein (DBP) or portions or fragments thereof, where thefusion protein comprises an amino acid sequence represented as(N-terminal portion of the LBP)-(DBP)-(C-terminal portion of the LBP),the method comprising preparing a library of fusion proteins byinserting the DBP randomly into the amino acid sequence of the LBP viaperforming a recombinant DNA method, and selecting a fusion protein fromthe library of fusion proteins as a biosensor.

Embodiment 22

The method of embodiment 21, wherein the recombinant DNA method istransposon-mediated DNA recombination.

Embodiment 23

The method of embodiment 21, wherein the fusion protein is selected as abiosensor via testing whether the fusion protein modulates transcriptionfrom a promoter comprising a binding site for the DBP of the fusionprotein in the presence or absence of the ligand for the LBP of thefusion protein.

EXAMPLES

The following Examples are illustrative and are not intended to limitthe scope of the claimed subject matter.

Example 1

Reference is made to Younger et al., “Engineering novel modularbiosensors to confer metabolite-responsive regulation of transcription,”ACS Synth. Biol. 2017 Feb. 17; 6(2):311-325, the content of which isincorporated herein by reference in its entirety.

Title: Engineering Modular Biosensors to Confer Metabolite-ResponsiveRegulation of Transcription

Abstract

Efforts to engineer microbial factories have benefitted from miningbiological diversity and high throughput synthesis of novel enzymaticpathways, yet screening and optimizing metabolic pathways remainrate-limiting steps. Metabolite-responsive biosensors may help toaddress these persistent challenges by enabling the monitoring ofmetabolite levels in individual cells and metabolite-responsive feedbackcontrol. We are currently limited to naturally-evolved biosensors, whichare insufficient for monitoring many metabolites of interest. Thus, amethod for engineering novel biosensors would be powerful, yet we lack ageneralizable approach that enables the construction of a wide range ofbiosensors. As a step towards this goal, we here explore severalstrategies for converting a metabolite-binding protein into ametabolite-responsive transcriptional regulator. By pairing a modularprotein design approach with a library of synthetic promoters andapplying robust statistical analyses, we identified strategies forengineering biosensor-regulated bacterial promoters and for achievingdesign-driven improvements of biosensor performance. We demonstrated thefeasibility of this strategy by fusing a programmable DNA binding motif(zinc finger module) with a model ligand binding protein (maltosebinding protein), to generate a novel biosensor conferringmaltose-regulated gene expression. This systematic investigationprovides insights that may guide the development of additional novelbiosensors for diverse synthetic biology applications.

Introduction

Cells evaluate and respond to their internal states through a range ofmechanisms, including the wide use of molecular biosensors. In a generalsense, a biosensor may be understood to comprise a species that sensesone or more analytes, typically through a molecular recognition eventinvolving binding to the analyte, such that recognition of the analyteis transduced into a change in the biosensor that enables it to effect achange in cell state. Biosensors may be composed of a range ofbiomolecules, most commonly including RNA¹⁻³ or protein⁴⁻¹⁵. Earlyapplications included the generation of whole-cell biosensors, in whichan environmental analyte enters a cell through active or passivetransport. Upon recognition of the analyte by an intracellularbiosensor, an output signal such as fluorescence, luminescence, orcolor-change is generated, most commonly by biosensor-induced expressionof a reporter gene or by analyte binding-induced changes in the activityof a fluorescent or enzymatic biosensor protein. A particularly excitingfrontier is the use of biosensors to sense not external factors, butrather a cell's internal metabolic state.

Metabolite-responsive biosensors may help to address several pervasiveand persistent challenges in the fields of synthetic biology andmetabolic engineering^(3-7, 9, 11, 13-16). First, biosensors may helpovercome the costliest and rate-limiting step in the development of newbiosynthetic pathways—screening and evaluating pathway or strainvariants to both identify well-performing constructs and glean insightsinto pathway function that may be utilized in subsequent iterativerounds of the design-build-test engineering cycle. By couplingmetabolite-binding to outputs such as fluorescence or antibioticresistance, biosensors can enable the screening of large libraries(e.g., >10⁸ members), which remain beyond the capacity of evencontemporary automated platforms for performing clonal evaluations. Forexample, the naturally occurring transcription factor BmoR was harnessedto confer growth in the presence of butanol, which enabled the screeningof a plasmid library to identify strains exhibiting robust production of1-butanol⁶. Similar approaches have been harnessed to screen plasmidlibraries to achieve enhanced production of mevalonate¹⁷, triacetic acidlactone¹⁸, and L-lysine¹⁹. Such an approach may be extended to screenfor high-performing variants generated through genomic mutation,including both random mutagenesis, which has been utilized to optimizeL-lysine production via mutation of endogenous enzymes²⁰, and targetedgenome-wide mutagenesis, which has been used to optimize naringenin andglucaric acid production via combinatorial perturbation of endogenousgene regulation⁴. While most investigations to date have applied thesemethods to bacterial chasses, such approaches may also be extended toyeast and other organisms⁹. In general, “digital” biosensor outputs,such as expression of antibiotic resistance, are most useful forscreening, while “analog” biosensor outputs, such as fluorescence,enable both screening and characterization of internal metaboliteconcentrations, potentially at the single-cell level, to guide constructanalysis and iterative refinement.

A powerful, yet less explored extension of this approach is the use ofmetabolite-responsive transcriptional regulators to implement feedbackcontrol in order to optimize system performance. An early demonstrationof this opportunity was the use of an acetyl phosphate biosensor tosense excess glycolytic flux, and in response, regulate the expressionof limiting genes in the lycopene biosynthesis pathway. This activityresulted in both enhanced lycopene production and diminished growthdefects¹⁶. More recently, feedback control was used to achieve balancedflux through several pathways that led to enhanced yields and improvedcell survival during the production of a biofuel (fatty acid ethylester)⁷. This investigation made use of the natural FadR biosensor,which is antagonized by Acyl-CoA, paired with synthetic promotersengineered to achieve robust regulation by FadR. Similarly,lysine-responsive riboswitches were utilized to control the expressionof citrate synthase and thereby increase lysine production bycontrolling flux in the TCA cycle³. The potential utility ofbiosensor-mediated feedback control is now widelyrecognized^(11, 21, 22), and further implementation is currently limitedlargely by the pool of suitable biosensors.

A general challenge in the use of biosensors is that the pool ofmetabolites one would like to measure and potentially utilize forfeedback control is much larger than the pool of metabolite-responsivetranscriptional regulators that have been identified. Bioinformaticapproaches and surveys of published literature may identify a number ofuseful biosensors that have simply not yet been utilized as such. Forexample, a recent study elegantly applied a systematic characterizationof known metabolite-responsive transcriptional regulators to generatequantitative fingerprints enabling these biological “parts” to beharnessed for engineering applications⁵. However, since the entire poolof naturally-evolved biosensors is likely much smaller than the pool ofmetabolite targets, it would be attractive to develop approaches forengineering novel biosensors. Ideally, such a biosensor could beconstructed to recognize an analyte of interest (with some practicaldegree of specificity), would exhibit a dynamic range suitable (ortunable) to the application of interest, and could be directed toregulate a gene (or genes) of interest in a ligand-dependent fashion.Although this comprises a daunting protein engineering challenge, anumber of smaller-scale successes suggest strategies that may help toachieve this goal.

The most widely used approach for engineering novel biosensors is togenetically fuse a ligand-binding protein with a distinct functionaldomain, such that the fusion causes the activity of the functionaldomain to be conditional upon the presence or absence of the ligand ofinterest^(10, 23-29). Most commonly, the functional domain comprises afluorescent protein or enzyme conferring antibiotic resistance, each ofwhich comprises an output amenable to screening the large librariesrequired to identify functional fusion proteins. For example, maltosebinding protein (MBP) and β-lactamase (BLA) were circularly permutatedto generate a library of fusion proteins, such that successful fusionsexhibited high BLA activity only in the presence of maltose²⁴.Calmodulin, which experiences a conformational change upon binding toCa²⁺, is similarly amenable to such a fusion strategy to create fusionproteins based upon BLA²⁶ or GFP and its derivatives³⁰. Indeed, manysimilar approaches have harnessed proteins in which ligand bindinginduces a conformational change in order to generate biosensors in whichfluorescence, often via FRET, provides a metric of intracellularmetabolite concentration (reviewed in^(10, 31-33)). Furthermore, zincfinger proteins (ZFP), transcription activator-like effectors (TALE),and CRISPR-based DNA binding domains have been fused to putativerepressor and activator domains to create novel transcription factors toregulate both prokaryotic and eukaryotic transcription, although suchfunctions are not generally regulated by ligand binding to thetranscription factor (³⁴⁻³⁹). However, recently an allostericallyregulated version of Cas9 has been developed by fusing the estrogenreceptor-α to create a protein that represses transcription in thepresence of the ligand, 4-hydroxytamoxifen⁴⁰. Given the broad homologywithin the LacI/GalR family of ligand-responsive transcription factors,novel biosensors have also been constructed by fusing the ligand-bindingdomains from Lad paralogs to the Lad DNA binding domain, conferringregulation of the lac promoter by fructose, ribose, or otherspecies^(17, 18, 41, 42). Ultimately, computational protein design couldguide the development of novel biosensors. To date, such methods havebeen used primarily to shift ligand specificity of existing biosensorproteins^(17, 43-47), although de novo design of novel ligand-bindingproteins and biosensors is another promising frontier^(48, 49) Overall,these approaches bespeak the promise of engineering novel biosensorproteins, but to date no generalizable approach for engineering novelmetabolite-responsive transcriptional regulators has been described.

In this study, we investigated, validated, and developed a strategy forengineering novel metabolite-responsive transcriptional regulators. Ourcentral goals were to quantitatively evaluate several strategies forconverting a ligand-binding protein into a functioning biosensor thatregulates transcription, and to elucidate design principles governingthe performance of biosensors constructed in such a fashion. To thisend, we leveraged the facts that MBP is a well-characterizedligand-binding protein, and that zinc finger proteins (ZFP) arewell-characterized and programmable DNA binding domains. Furthermore, weapplied quantitative analyses to identify rules for designingbiosensor-regulated promoters and quantitatively characterize these newbiological parts. This systematic investigation establishes a foundationfor applying a potentially generalizable strategy towards the ultimategoal of engineering customized metabolite-responsive bio sensors.

Results

Developing Novel Zinc Finger Protein-Regulated Constitutive Promoters.

In this investigation, we sought to develop a readily generalizablestrategy for engineering novel biosensor proteins from the ground up. Wehypothesized that such a goal might be achieved by first using anorthogonal DNA binding protein to regulate transcription of anengineered promoter, and then fusing this DNA binding domain to adistinct protein capable of binding the target ligand, such that whenthe fusion protein binds ligand, DNA binding (and thus transcriptionalregulation) is either disrupted or enhanced.

To begin investigating this overall strategy, we first sought toengineer a novel transcriptional regulator by leveraging the modular,programmable DNA binding properties conferred by the zinc finger protein(ZFP) architecture⁵⁰⁻⁵². ZFPs are small (compared to alternativearchitectures such as TALEs^(38, 39)), easy to manipulate, and can bedesigned to bind to nearly any sequence. The ZFP architecture haspreviously been utilized to create novel transcription factors in E.coli ³⁶, as well as in eukaryotes^(34, 35, 53). The Cys₂-His₂ class ofZFPs is an attractive DNA binding domain, since each “finger” of the ZFPbinds to a distinct 3 bp DNA sequence (FIG. 1A)⁵⁴, and thus sequencespecific binding is achieved by engineering ZFPs comprising multiple“fingers” fused in tandem. Therefore, to initially investigate ourstrategy for biosensor engineering, we utilized the BCR-ABL1 ZFP as ourDNA binding domain. BCR-ABL1 is well-characterized, exhibits a lowequilibrium dissociation constant when binding its cognate 9 bp DNAbinding site with three tandem fingers (K_(d)˜78 μM), and has been shownto exhibit conditional DNA binding when genetically split andreconstituted^(50, 55) Notably, although the BCR-ABL1 consensus bindingsequence is known, no E. coli promoters have been previously repressedby BCR-ABL1.

In order to begin elucidating the rules for building novelZFP-regulatable promoters, a library of 68 different constitutivepromoters was designed (FIG. 7). The library was built by insertingBCR-ABL1 binding site(s) at various locations around the consensus −10box (TATAAT) and −35 region (TTGACA). The −10 box and −35 region arecritical for recruitment of the σ⁷⁰ factor of RNA polymerase, and thesesequences are necessary and sufficient to create a constitutivepromoter^(56, 57) Design features that varied across the libraryincluded BCR-ABL1 binding site location(s), relative to the consensuselements, and spacing between BCR-ABL1 binding sites. Although thisdesign did not presuppose that ZFP-binding would repress transcriptionfrom these constitutive promoters, this was the anticipated mechanismbecause BCR-ABL1 was not fused to a transactivation domain³⁶. Eachpromoter was encoded on a low copy number plasmid and drove expressionof E. coli-optimized GFP as a reporter.

To quantify the extent to which each promoter was repressed or activatedby BCR-ABL1 during exponential growth, we defined a metric of “relativeexpression” that describes how induction of BCR-ABL1 expression impactsexpression from the promoter as compared to a “No Sites” controlpromoter lacking BCR-ABL1 binding sites (see Material and Methods). Thisrelative expression normalization strategy was utilized in order toimplicitly correct for any effects that arabinose many confer onGFP/OD₆₀₀ in a manner unrelated to expression of the ZFP. Thus, lowrelative expression indicates that a promoter is highly repressed byBCR-ABL1. The library of promoters exhibited wide ranges of basalexpression (FIG. 8) and repressibility by BCR-ABL1 (FIG. 1B). Nearly allof the promoters exhibited a decrease in GFP expression upon theinduction of the BCR-ABL1 ZFP, and no promoters showed any level ofactivation upon BCR-ABL1 induction. When relative expression wasquantified at 10 h post-induction, at which point cultures had reachedstationary phase, the observed repressibility was much more pronounced(FIG. 1C). To investigate how relative expression patterns differedbetween cells within each population, we also examined severalrepresentative promoter cases at the single cell level using flowcytometry (FIG. 1D). Overall, responses were unimodal, such thatpopulation-averaged fluorescence measured by flow cytometry correspondedwell to comparable metrics obtained by microplate-based assays (FIG. 9),and thus the latter method was used for subsequent analyses. Given thiswide range of phenotypes, we next investigated the relationship betweenpromoter design and repressibility.

We first examined the impact of promoter design on repressibility byinspection. As depicted in FIG. 2, promoters representing variations ona particular design feature were first grouped together in order toidentify simple trends, with the caveat that such trends are potentiallyrestricted to the scope of promoters evaluated in our library.Generally, placing two ZFP binding sites in between the −10 box and the−35 region (the promoter “core”) led to greater repressibility than didinsertion of a single ZFP binding site, regardless of its position (FIG.2A). Go15 is an exception to this trend, as it was not dramatically lessrepressible than was Go14. Furthermore, promoters with two ZFP bindingsites in the core were more repressible than were comparable promotersthat lacked these core sites but shared other ZFP binding sites (FIG.2B). Compared to promoters including all three ZFP binding sitesrequired for recognition by BCR-ABL1, promoters in which only two siteswere present exhibited reduced repressibility by BCR-ABL1 (FIG. 2C).Adding additional ZFP binding sites downstream of the −10 box did notincrease repressibility (FIG. 2D). However, adding additional bindingsites upstream of the −35 box did effectively increase repressibility(FIG. 2E). Decreasing the spacer distance between the −10 box and thefirst downstream binding site increased repressibility (FIG. 2F).Similarly, removing any spacer between the ZFP binding sites and the −10box and −35 region resulted in greater repressibility than did removingspacers adjacent to either the −10 box or −35 region alone (FIG. 2G). Toexplain the increase in repressibility observed for Go85 compared toGo66, we hypothesize that BCR-ABL1 cannot simultaneously occupy adjacentbinding sites, and furthermore, that binding to the sites directlyupstream of the −10 box and downstream of the −35 region results ingreater repressibility than is conferred by BCR-ABL1 binding to the moredistal binding sites present in Go66. Altogether, promoter Go92exhibited the greatest repressibility of any library member, and thispromoter appears to follow many of the design rules suggested by theabove inspection-based analysis. However, since only a subset of ourpromoter library was amenable to this direct inspection-based analysis,we next pursued a systematic analysis of the entire library to furtherrefine our understanding of the applicable design rules.

Computational Identification of Promoter Design Features ConferringZFP-Mediated Repression.

Statistical methods can provide insights into large or diverse data setsthat are difficult to compare qualitatively or by inspection alone.Therefore, we performed a series of statistical analyses termedcomputational “feature selection” in order to determine which promoterfeatures are important for predicting the relative repressibility of agiven promoter in the presence of the ZFP. Given a set of feature“inputs,” feature selection seeks to eliminate those features that areredundant or irrelevant to the prediction of a particular output. In ouranalysis, the output was defined as the repressibility (the negative ofrelative expression) exhibited by each promoter in the library. Togenerate the input list, we defined a set of 17 quantitative featuresthat described each promoter in the library. Because we sought toelucidate general design rules and avoid over-fitting our particularpromoter library, we defined the 17 features strictly on the basis ofdescribing the locations of each ZFP binding site relative to the −10box, the −35 region, and to other ZFP binding sites (FIG. 3A).

Three different feature selection methods were applied to analyzeBCR-ABL1-mediated repression of our promoter library. We first usedpartial least squares regression (PLSR), in which the regressioncoefficient associated with each feature indicates the degree to whichthat feature explains variations in repressibility within our dataset.Each coefficient was scaled using a permutation test to correct for thecoefficient one would calculate for a randomized (meaningless) outputvector⁵⁸, and these corrected coefficients were used to generate aranked lists of features (for PLSR coefficients, see FIG. 10). Byrepeating the PLSR using only fixed numbers of most important features,we found that the first few features explained much of the variance inthe overall dataset (FIG. 3B). A similar trend was observed whenrepeating the PLSR with a limited number of principal components,indicating that principal components do not provide deeper understandingof this system than do single features. The second method used wasRandom Forest, which creates a predictive model of the system usingdecision trees. By iteratively generating decision trees using a subsetof features, this method calculates the mean loss of accuracy when agiven feature is removed, and this quantity is used to generate a rankedlist of features by importance (FIG. 10). The final method used wasLasso regression, which performs least squares regression with anadditional penalty placed on the magnitudes of regressioncoefficients⁵⁹. This penalty is weighted by a parameter, λ, such that asλ is increased, the coefficients of unimportant features shrink to zero;features are thus ranked by the number of iterations that they retain anon-zero coefficient as λ is increased. For each value of λ, theregression fit was tested with 10-fold cross validation, to obtain amean squared error (MSE) and number of retained coefficients for eachvalue of λ (FIG. 3C). A reasonable fit of the system was obtained using3-5 features, with lower MSE for larger feature numbers likelyrepresenting over-fitting of noise in the dataset.

Overall, the three feature selection methods (PLSR, Random Forest, andLasso) generated similar but not identical ranked lists of features(FIG. 3D). The most important features included spacer inside −10 andspacer −10, which were undesirable for repressibility, and BS in core,core middle space, and pairs in core, which were desirable forrepressibility. Together, these findings indicate that placing bindingsites as close as possible to the −10 box and −35 region are predictedto confer strong repressibility. Interpreting other features is lessstraightforward, such as core middle space, which describes whetherthere exist base pairs in the core other than ZFP binding sites; thethree methods appear to disagree on the importance of core middle space.One possible explanation for this discrepancy is the influence of Go22and Go92 on this analysis, since both of these promoters contain spacein the core but no space on either side of the −10 box (Go22 and Go92)and no space on either side of the −35 region (Go92). As such, for thesepromoters, containing space in the core may be merely associated withthe presence of other promoter features conferring repressibility. Thesecases may well emphasize the importance of spacer inside −10 and spacer−10. Moreover, Lasso, which is considered the most robust method forfeature selection, ranked core middle space as unimportant, potentiallyby resolving this contradiction better than did the other methods.Altogether, this analysis enabled us to leverage our diverse promoterlibrary to glean general, quantitative design principles for engineeringZFP-repressible promoters. However, it was not yet clear whether suchrules would extend to the design of promoters regulated by ZFP-basedbiosensors.

Conversion of Transcriptional Repressors into Ligand-ResponsiveBiosensors.

Having established that BCR-ABL1 functions as a transcriptionalrepressor, we next investigated two strategies for converting thisrepressor into a biosensor. Here, the primary goal was to investigategeneral strategies for converting a ligand-binding protein into aligand-responsive transcription factor. As described above, wehypothesized that such conditional regulation of gene expression may beachieved by fusing BCR-ABL1 to a ligand-binding domain. To investigatethe feasibility of this approach, we chose the uniquely well-studiedmaltose binding protein (MBP), in part because this protein experiencesa substantial and well-characterized conformational change (˜9 Ådecrease in separation between N and C termini) upon ligand binding⁶⁰⁻⁶⁵The first strategy we explored was termed the Split Zinc Finger (SZF)approach, in which BCR-ABL1 was split genetically such that the N and Ctermini of MBP were fused to BCR-ABL1-derived ZFPs. This strategyleverages prior observations in which the N and C termini of MBP werefused to FRET-paired fluorophores⁶⁶⁻⁶⁹ split GFP fragments²⁸, orcontext-dependent fluorophores⁴⁵, each enabling the monitoring of ligandbinding-induced conformational changes in MBP. Further rationale forthis strategy is that ZFPs exhibited conditional DNA binding when thisdomain was genetically split and then reconstituted using eitherself-splicing inteins or protein-protein interactions^(55, 70). Thesecond strategy we explored was termed the Split Protein (SP) approach,in which MBP was genetically split, with the halves fused to the N and Ctermini of intact BCR-ABL1. We hypothesized that such a construct maypermit ZFP-DNA interactions in a manner that depends upon whether MBP isbound to maltose. The three most repressible reporters (Go66, Go85, andGo92) were used to evaluate the feasibility of each of these proposedbiosensor mechanisms.

To investigate the SZF strategy, BCR-ABL1 was split between the firstand second zinc fingers as previously described⁵⁵ (see FIG. 4A for theproposed mechanism). None of the reporters evaluated exhibitedrepression upon the induction of SZF biosensor expression (FIG. 4B). Onepotential explanation is that the three zinc fingers could not localizewith the spacing or geometric orientation required to simultaneouslybind a single BCR-ABL1 DNA binding site. While it may be possible toimprove SZF biosensor-mediated repression by identifying promoters thatplace BCR-ABL1 binding sites in geometries that enable a split ZFP tobind, there is no guarantee that such a configuration would exist.Moreover, even if such a promoter were identified, such a design islikely to be a “one-off” solution specific to the MBP SZF biosensor.Therefore, we next evaluated the alternative SP biosensor designstrategy, which has the potential to be more generalizable.

To initially investigate the SP strategy, MBP was genetically split atthe point previously reported to generate a functional chimera withbeta-lactamase (BLA), termed “RG13”²⁴, and BCR-ABL1 was inserted betweenthese N and C terminal fragments (see FIG. 4C for the proposedmechanism). We hypothesized that such a split site might support the SPmechanism because (a) in RG13, MBP retains the capacity to bind maltoseand (b) in RG13, binding of maltose to MBP likely induces aconformational rearrangement of the overall fusion protein (or at leastthe stabilization of a conformation that alleviates disruption of theBLA active site⁷¹). This SP biosensor was again evaluated against aselect set of reporters. Notably, induction of biosensor expressionsuppressed reporter output in a manner that was significantly alleviatedin the presence of maltose (FIG. 4D). These data thus support thefundamental feasibility of the SP strategy. This observation is alsoconsistent with our hypothesis that DNA binding was impaired in the SZFarchitecture, at least compared to the SP architecture. Notably,repressibility conferred by the SP biosensor was only slightly less thanthat observed with BCR-ABL1 ZFP alone (FIG. 4E). This correspondencesuggests that the rules for predicting robust ZFP-mediated repression ofreporter output (FIGS. 2, 3) seem to hold true for the SP biosensor,which also supports the generalizability of the SP strategy with respectto reporter construct design. As was observed for BCR-ABL1-mediatedrepression (FIG. 1D), SP biosensor-mediated repression and alleviationwas also unimodal when analyzed by flow cytometry (FIG. 11).

Given these promising initial results, we next investigated how themethod of SP biosensor implementation impacts the performance of thesystem. We first investigated how biosensor expression levels impactperformance (FIG. 4F). With increasing biosensor expression levels,repression of the promoter increased (bottom curve), althoughmaltose-mediated alleviation of reporter output also decreased (uppercurve). Such a trend is the expected behavior of this system if, athigher levels of biosensor expression, intracellular concentrations ofmaltose are insufficient to drive all of the biosensors into theligand-bound (alleviated) state. We would propose two hypotheses thatcould explain this phenomenon. First, at higher concentrations ofmaltose, transport limitations may confer an upper bound on theintracellular concentration of maltose, irrespective of theextracellular maltose concentration. Second, it remains possible thateven at saturating intracellular concentrations of maltose, themaltose-bound form of our biosensor may still bind DNA (and represstranscription) to a lesser but finite extent, compared to the apo formof the biosensor. Thus, we determined that for the case of a relativelyhigh extracellular concentration of maltose (100 mM in medium, which isexpected to be substantially higher than the concentration in thecytoplasm), induction of biosensor expression with 30 μM IPTG conferreda robust balance between repression and maltose-mediated alleviation ofreporter output. To exclude the possibility that some other aspect ofcell biology or ZFP function could explain the maltose-mediatedalleviation of reporter output, a similar analysis was performed usingcells expressing the ZFP alone (i.e., in place of the SP biosensor). Asexpected, the addition of maltose had no significant impact onZFP-mediated repression of the reporter (FIG. 12). Moreover, theaddition of maltose did not substantially impact cell growth (FIG. 13).We next investigated how extracellular maltose concentration impactsbiosensor performance (FIG. 4G). With the caveat that intracellularconcentrations of maltose are expected to be substantially lower thanare extracellular concentrations, we observed that biosensorresponsiveness varied with extracellular maltose concentration, and forthe most sensitive reporters (Go92 and Go85), modest but significantalleviation was observed at extracellular maltose concentrations as lowas 0.1 mM. To further investigate how maltose binding to MBP domainsimpacts SP biosensor performance, we repeated this dose-responseanalysis after making a mutation in the MBP domain (W340A), which hasbeen reported to substantially weaken, but not ablate, MBP binding tomaltose⁷² (FIG. 4H). As expected, a substantially higher (˜400×)extracellular concentration of maltose was required to alleviatereporter output in the W340A SP mutant case. Altogether, theseobservations demonstrate the feasibility of engineering novel biosensorsusing the SP strategy. Although the MBP split site used in this initialconstruct was not selected to optimally implement the SP strategy, ouroverall goal was to evaluate the SP strategy in general. Therefore, wecarried this functional SP biosensor forward for further development.

Contributions of Biosensor Biophysical Properties to BiosensorPerformance.

In order to investigate how general biophysical properties of abiosensor impact its performance, we next performed a series of rationalmodifications of the SP biosensor protein. First, we hypothesized thatif DNA binding affinity limits the degree to which our biosensorsrepress transcription, then replacing the BCR-ABL1 domain with a ZFPthat binds to DNA with higher affinity would improve transcriptionalrepressibility in the absence of maltose. However, since BCR-ABL1interacts with its binding site with a K_(d)˜78 pM⁵⁰, a simple model ofbinding equilibrium would suggest that promoter occupancy should notvary much with changes in this high affinity binding constant. As apoint of reference, we note that the dimeric tetracycline repressor(TetR) binds to its operator sequence (tetO) with a similar K_(d)˜20pM⁷³, although tetR is understood to achieve exquisite transcriptionalrepression through contorting the target DNA rather than through highaffinity binding alone⁷⁴. In order to directly investigate therelationship between affinity and repression in our system, and toinvestigate the modularity of our biosensor vis-à-vis ZFP domain choice,we replaced BCR-ABL1 with the Zif268 ZFP domain from the human EGR1protein. Zif268 binds its 9 bp binding site with a K_(d)˜8 pM (˜10 timestighter than that of BCR-ABL1)⁵⁰. Go92 was converted toZif268-responsive promoter by replacing the BCR-ABL1 binding sites withZif268 binding sites (GCAGAAGCC versus GCGTGGGCG, respectively). The SPbiosensor was also modified to replace the BCR-ABL1 ZFP with Zif268(SP-Zif268). SP-Zif268 did not exhibit an enhanced capacity to suppressreporter output, although it instead exhibited reduced fold-alleviationin the presence of maltose compared to the original SP biosensor (FIG.5A). Altogether, these observations are consistent with a simple modelwherein increasing the affinity of the biosensor for its target DNA didnot increase repression, presumably because such a change would notimpact promoter occupancy. Moreover, the fact that the SP-Zif268biosensor exhibited significant (if somewhat diminished) functionalityindicates that the ZFP domains within SP biosensors may be exchanged ina modular fashion.

We next investigated how biosensors size may impact reporter repression,for example by sterically occluding RNA polymerase binding to the −10box and −35 region. To this end, the fluorescent protein mCherry wasfused to either the N- or C-terminus of the SP biosensor to generatemC-SP or SP-mC, respectively. In this experiment, mCherry was selectedas a functionally “neutral” fusion partner in order to investigate theimpact of increasing the bulk of the biosensor alone. Although the SP-mCmodification did not improve biosensor performance, the mC-SP constructnotably exhibited both improved repression and increased fold-inductionof reporter output upon the addition of maltose (FIG. 5). Altogether,these data suggest a useful strategy for building novel biosensors inwhich the tradeoff between desired performance characteristics may beoptimized for a particular application. In general, adding steric “bulk”may outperform enhancing DNA binding for increasing repression in the“off” state without sacrificing the degree to which the regulated geneis expressed when in the “on” state.

Discussion

In this study, we investigated a potentially generalizable strategy forconverting metabolite-binding proteins into metabolite-responsivetranscription factors. By systematically and quantitatively evaluatingthe design principles governing the performance of such biosensors,which was the focus of this investigation, this work establishes afoundation for pursuing the long-term goal of engineering repertoires ofcustomized metabolite-responsive biosensors. By leveraging modulardesign of both promoter libraries and biosensor proteins, theseinvestigations elucidated a number of design principles that are usefulfor both explaining the variations observed in our libraries and forguiding the design of novel biosensors in subsequent work.

Using a library of engineered promoters, we identified several importantrules by which binding of a ZFP to DNA confers a repression oftranscription. Interestingly, nearly all promoters evaluated wererepressed, at least to some degree, and none exhibited increasedexpression in the presence of the ZFP. The BCR-AB1 ZFP alone wassufficient to achieve significant transcriptional repression, eventhough this protein is smaller than canonical natural transcriptionfactors, such as TetR and Lad (106 aa compared to 221 aa (TetR) and 374aa (Lad)). This minimal ZFP also regulated gene expression in mannersomewhat different from that conferred by a previously described fusionbetween a ZFP and a transactivation domain from CRP. Lee at al. observedthat this ZFP-CRP fusion conferred transcriptional activation when boundupstream of the +1 site and repression when bound downstream of the +1site³⁶, while our minimal ZFP (which lacks a transactivation domain)conferred repression even when bound upstream of the +1 site (FIG. 2).We determined that placing ZFP binding sites as close as possible to theconsensus −10 box and −35 region of the promoter yielded the highestlevel of transcriptional repression. The −10 box and −35 region are thesites at which the transcription initiation factor σ70 binds in order tomediate recruitment and assembly of the RNA polymerase (RNAP) complex.Therefore, we hypothesize that placing the ZFP binding sites very closeto the −10 box and −35 region effectively prevents the σ70 from bindingto this region of DNA and/or σ70-mediated recruitment of RNAP. Thegreatest repression was observed when both the −10 box and −35 regionwere abutted with ZFP binding sites, and blocking the −10 box may confergreater repression than does blocking the −35 region (FIGS. 2G, 3D). Itis possible that these observations may be leveraged to achieve greaterrepression by overlapping the ZFP binding sites with the conserved −10box and −35 region, since binding of biosensors to these sites may moreefficiently block σ70-mediated recruitment of RNAP. One potentialchallenge associated with this strategy is the potential to repressendogenous genes that share the consensus −10 box and −35 regions,although minimizing overlap with these consensus sequences couldmitigate this problem.

Our comparison of two potential biosensor engineering strategies—the SP(split protein) and SZF (split zinc finger) architectures—revealedseveral insights into the feasibility and generalizability of eachapproach. The SP biosensors repressed the most-repressible reporters tonearly the same extent as did the ZFPs alone, suggesting that the rulesgoverning promoter design may be generalizable across SP biosensors(FIG. 4D). In contract, the SZF biosensor evaluated conferred norepression of reporter output (FIG. 4B). We hypothesize that insertionof MBP between the fingers of BCR-ABL1 precluded simultaneous binding ofDNA by all three zinc fingers. Even if this geometric constraint werealleviated by modulating the protein or DNA sequences, it is likely thatsuch a solution would be unique to each biosensor. Therefore, the SZFapproach may be generalizable, but not readily so. In contrast, the SPapproach was both more effective and may also be more readilygeneralizable.

Our investigation also provided several insights into the mechanism bywhich this initial SP biosensor functions and the prospects forextending this approach to generate novel biosensors. In many ways,these insights leverage the wealth of information available to describeour model ligand-binding domain, MBP. The SP biosensors utilized the MBPsplit sites that were identified by using a random domain insertionapproach to generate the “RG13” MBP/BLA fusion protein²⁴; the N terminalhalf of SP comprises the first 316 aa of MBP, and the C terminal halfcomprises residues 319-370 of MBP. In the crystal structures of both MBPand RG13, residues 316R and 319A are ˜10 Å apart^(62, 71). However, itshould be noted that the RG13 crystal structure was obtained in thepresence of saturating Zn²⁺, a condition which ablated the activity ofthe BLA subdomain of the protein, and that no maltose-bound (orzinc-free) structure of RG13 has been obtained. Thus, these distancesshould be treated as estimates as to how RG13 residues 316R and 319A arepositioned when the protein is expressed under physiological conditions.When MBP binds maltose, the separation of these residues increases by nomore than ˜3 Å⁶⁰. In contrast, when a Cys₂-His₂ class ZFP binds to itscognate 9 bp of DNA, the distance separating the N- and C-termini of theZFP is ˜40 Å⁷⁵. Therefore, we hypothesize that in order for the ZFPdomain of the SP biosensor to adopt a conformation capable of bindingits 9 bp DNA target, residues 316R and 319A may be separated by as muchas 40 Å. Furthermore, since the addition of maltose alleviatesbiosensor-mediated repression of transcription (and therefore impairs orablates DNA binding), we hypothesize that maltose binding to the SPbiosensor stabilizes interactions between the split MBP fragments, suchthat residues 316R and 319A are retained in a close (˜13 Å) spacing,which prevents the ZFP domain from adopting a conformation capable ofDNA binding (FIG. 4C). Importantly, if the SP biosensor operates viathis ligand binding-induced stabilization mechanism, then biosensorfunction need not rely upon a ligand-binding induced conformationalchange in MBP. Thus, this mechanism could be extended to ligand-bindingproteins that do not experience a ligand binding-induced conformationchange as dramatic as that exhibited by MBP. Moreover, the proposedligand binding-induced stabilization mechanism is consistent with the“induced fit” model of substrate binding, in which ligand binding causesa shift in protein structure that results in an increase in thestability of the ligand-bound complex. Indeed, ligand binding-inducedstabilization may confer allosteric regulation of many proteins, andthis property may even be engineerable⁷⁶. Thus, we speculate that themechanism of the MBP-based SP biosensor may be extended to biosensorsbased upon distinct ligand-binding domains. Moreover, there exist manymethods by which proteins can be split, fused, and screened, includingin vitro methods such as circular permutation and domain insertions, aswell as computational methods for predicting effective split sites, suchthat evaluating whether a given ligand-binding protein is amenable toconversion into a biosensor using the SP approach is relativelystraightforward^(25, 77-80). In fact, periplasmic binding proteins suchas MBP may be generally amenable to conversion into molecular switchesvia domain insertion, as was demonstrated by the insertion of TEM-1beta-lactamase into ribose binding protein, glucose binding protein, andxylose binding protein⁸¹. Additionally, an allosterically regulatedversion of Cas9 has been developed by using domain insertion to fuseCas9 to estrogen receptor-α to create a repressor that is inducible bythe addition of 4-hydroxytamoxifen⁴⁰. In this study, a site in Cas9 thatis permissive to protein insertion was first identified using randomdomain insertion of a PDZ domain. Thereafter, the estrogen receptor-αwas inserted into this site following the rationale that this receptorundergoes a substantial conformation change upon ligand binding,bringing its termini within 21 Å of one another in the presence ofligand, such that only the ligand bound conformation of the receptor mayexhibit a structure that avoids disruption of the Cas9 structure (and,presumably, function). Thus, while this technology is of substantialutility for regulating Cas9, it is not yet clear whether or how thisapproach may be extended to generate Cas9-based regulators that areresponsive to a range of metabolites or ligands. Altogether, the SPstrategy appears to be a promising and potentially generalizable methodfor generating novel biosensors, although further investigation isrequired to determine which types of ligand-binding proteins may be mostreadily converted into biosensors via this approach.

Our investigation also provided several insights into how biophysicalproperties of the biosensor itself could impact its overall performance.First, comparing SP biosensors based upon BCR-ABL1 to those based uponZif268, the latter of which binds its cognate DNA with approximately10-fold greater affinity, we observed that the SP-Zif268 biosensorrepressed transcription to a similar extent but exhibited a reducedresponse to the addition of maltose. As discussed above, the observedcomparable degree of repression is consistent with a simple model ofhigh affinity binding, in which both SP and SP-Zif268 biosensors achievea similar level of promoter occupancy. To interpret the reduced responseto maltose, we hypothesize that due to the tighter binding of Zif268 toDNA, even the maltose-bound state may interact with DNA to some extentthat represses reporter output (indeed, the same may be true to a lesserextent for the original SP biosensor). For example, if eachmaltose-bound biosensor exists in an equilibrium between states that arecompetent (disfavored) versus incompetent (favored) for DNA binding,then the higher affinity with which Zif268 binds DNA may causebiosensors based upon this protein to become “trapped” in a DNA-boundstate, even when bound to maltose. Finally, the fact that the SP-Zif268biosensor nonetheless exhibited significant (if somewhat diminished)functionality indicates that, within the SP framework, the ZFP domainsmay be exchanged to tune biosensor performance or to regulate novelreporter constructs.

We also investigated the role of biosensor size on performance, whichprovided some insights into how biosensor performance may be tuned. Weobserved that fusing mCherry to the N terminus of the SP biosensor(mC-SP) improved both reporter repression and fold induction upon theaddition of maltose, although no such effect was observed when mCherrywas fused to the C terminus of the SP biosensor (SP-mC). While it is notpossible to provide a specific structural explanation for these effects,a reasonable speculation is that the mC-SP biosensor sterically occludesrecruitment of the RNAP to a greater extent than does the original SPbiosensor. If this were true, it could be possible to achieve evengreater repression of reporter expression by exploring the addition of“bulky” domains of various sizes, shapes, and linker geometries to acandidate SP biosensor.

We also attempted to compare the performance of our initial SPbiosensors to that of some naturally-evolved biosensors, using thesystematic characterization of the latter that was recently reported byRogers et al.⁵. Rogers et al. evaluated fold-induction after cells hadreached stationary phase, while we evaluated both repression andalleviation during exponential growth, so we re-analyzed our data fromthe experiments reported in FIG. 5 using a later time point at whichcell growth had slowed, to facilitate this comparison (FIG. 14). Whilethis analysis did indeed lead to higher calculated values for degree ofrepression and fold-alleviation upon the addition of ligand (6±0.4fold-repression and 3.8±0.3 fold-alleviation for the mC-SP biosensor),the natural biosensors generally achieved a greater fold-induction, duein large part to the more efficient suppression of output geneexpression when in the ligand-free “off” state. While such nuancesreflect the manner in which biosensor performance is evaluated to someextent, this investigation more importantly identifies specificperformance attributes of SP biosensors that might be targeted to betterapproach the performance of naturally-evolved biosensors.

Although we evaluated and identified several promising strategies forimproving biosensor performance, it is possible that when extending theSP approach to target applications, biosensor performance may be furtherimproved by either design-driven or screening-based methods. Somestrategies could entail refining reporter design. Depending on theapplication requirements, fold-induction may be improved by locating thereporters on single-copy plasmids or on chromosomal DNA (instead of onlow copy number plasmids as described here) to increase promoteroccupancy for a given quantity of biosensors. Alternatively, thepromoter sequence could be altered to partially diminish interactionswith 670, potentially using either targeted mutations or random promotermutagenesis followed by selection to “tune” a promoter to match theproperties of a given biosensor. Other strategies could improve thebiosensor proteins. In particular, although utilizing the RG13 splitsite for MBP proved to be feasible for generating our initial SPbiosensors, it is likely that evaluating all possible ZFP insertionsites into a ligand binding protein may identify fusion proteins thatare specifically suited to the SP mechanism. Based upon our observationsof the factors limiting SP biosensor performance, candidate biosensorsmay also be improved by random mutation and directed evolution (e.g.,optimizing allosteric regulation to enhance ligand binding-inducedalleviation of DNA binding). A final strategy could be to process theoutput of our existing biosensor/reporter(s) system to achievepreferable overall performance characteristics. For example, reporteroutput could be coupled to additional genetic circuitry, such asRNA-based toe hold switches or positive feedback circuits to amplifyreporter output, and with some tuning, increase fold-induction^(82, 83).By leveraging the modularity conferred by programmable ZFPbinding^(50, 75) it may also be possible to implement multiple SPbiosensors in a single cell. Moreover, high throughput genomeengineering approaches such as MAGE⁸⁴ could make it possible to placeeven endogenous genes under partial or total control of such engineeredbiosensors. In sum, a modular approach to biosensor engineering islikely to accelerate the generation of novel biosensors, iterativeimprovement of biosensor performance, and adaptation of biosensors fornovel applications in metabolic engineering and synthetic biology.

Materials and Methods

Bacterial Strains and Culturing.

All experiments were conducted in TOP10 Escherichia coli cells (F-mcrAΔ(mrr-hsdRMS-mcrBC) φ80lacZAM15 ΔlacX74 nupG recAl araD139Δ(ara-leu)7697 galE15 galK16 rpsL(Str^(R)) endA1 λ⁻) (LifeTechnologies). Cells were maintained in Lysogeny Broth (LB) Lennoxformulation (10 g/L of tryptone, 5 g/L of yeast extract, 5 g/L of NaCl)supplemented with appropriate antibiotics (Ampicillin 100 μg/mL orKanamycin 50 μg/mL). All experimental analysis was conducted in M9minimal media (1×M9 salts, 0.2% Casamino Acids, 2 mM MgSO⁴, 0.1 mMCaCl², 1 mM Thiamine HCl) containing glycerol (0.4%) as the primarycarbon source. 1% arabinose and variable amounts of maltose monohydrateand isopropyl β-D-1-thiogalactopyranoside (IPTG) were added asindicated. M9 medium containing both Ampicillin and Kanamycin was usedto maintain the strains that contained both a reporter plasmid and abiosensor plasmid.

Plasmid Construction.

All plasmids were assembled using standard molecular biology techniques.Plasmid backbones containing “plug-and-play” multiple cloning sites andcompatible plasmids containing synthetic parts (mCherry, GFPmut3b, pBAD,AraC, pTrc2) were generously provided by Jim Collins (MIT)⁸⁵. Custom RBSsequences were designed using the RBS Calculator⁸⁶. The pA15 low copynumber origin was obtained from the Registry of Standard BiologicalParts, plasmid pSB3K3. Template sequences derived from publisheddescriptions were used for terminators⁸⁷, BCR-ABL1⁵⁵, and the Zif268portion of human EGR1⁸⁸ (AddGene #52724). MBP was PCR amplified directlyfrom TOP10 genomic DNA. The library of constitutive reporters was clonedin a low copy number pA15 backbone (˜10 copies per cell) with theampicillin resistance cassette. All of the pBAD-based inducible ZFP andpTrc-based inducible biosensor expression constructs included a ColE1backbone (˜300 copies per cell) and kanamycin resistance cassette. ThemCherry gene was cloned behind each ZFP or biosensor gene to act as aco-cistronic reporter to confirm arabinose and IPTG mediated inductionof gene expression. Representative plasmid maps are included in FIG. 6.

Microplate-Based Fluorescence Assays and Analysis.

Cultures were inoculated from single colonies into 2 mL of M9 media andgrown overnight to stationary phase. Overnight cultures were diluted1:20 and grown into exponential phase (OD₆₀₀˜0.5). Cultures were againdiluted to an OD₆₀₀˜0.05, plated in black-walled clear bottom 96-wellplates in biological triplicate, and induced with 1% arabinose (to driveexpression of the ZFP) or IPTG as indicated (to drive expression of thebiosensor), +/−maltose as specified. In each experiment, IPTG-inducedexpression of biosensor constructs was confirmed via the co-cistronicexpression of mCherry (data not shown). Plates with lids were incubatedand shaken in a continuous double orbital pattern at 548 cpm (2 mm)inside a BioTek Synergy H1 plate reader for 10 h with GFP, mCherry, andOD₆₀₀ measurements taken every 15 min. Monochrometer settings were481/511 nm for GFP and 585/620 nm for mCherry.

To quantify reporter output, GFP fluorescence per OD₆₀₀ was quantifiedand averaged over 7 time points that span ˜1.5 h of exponential growth(unless otherwise indicated). The specific fluorescence of each samplewas defined as the mean (GFP/OD₆₀₀) averaged across these 7 time points,and this specific fluorescence was averaged across 3 biologicalreplicate samples. To quantify fluorescence attributable to GFP, eachsample was background-subtracted using a control sample comprising cellsexpressing no fluorescent proteins. To enable comparisons betweenpromoters, each specific fluorescence value from the arabinose orIPTG-induced condition was normalized to the specific fluorescence ofthe uninduced condition, yielding “relative specific fluorescence”. Tonormalize this metric of promoter performance to the base case, therelative specific fluorescence calculated for each promoter-ZFP (orpromoter-biosensor) combination was then normalized to the same valuecalculated for that repressor using the “No sites” promoter, yielding aquantity we termed, “relative expression”. This normalization strategywas utilized in order to implicitly correct for any minor effects thatarabinose, IPTG, or maltose many confer on GFP/OD₆₀₀ in a manner that isunrelated to expression of the ZFP or biosensor. Thus, when quantifyingbiosensor performance, relevant control samples for the “+maltose” case(e.g., the uninduced case and No sites control case) were alsoquantified in the presence of maltose. For each metric, error waspropagated according to the division rule to generate reported standarddeviations.

Flow Cytometry.

Flow cytometry was used to quantify fluorescent reporter output on asingle cell basis. Cells were grown and induced as described for themicroplate-based fluorescence assays. Samples were collected after 5hours of growth. Cells were then placed on ice, diluted 1:2 in chilledphosphate buffer saline (PBS) supplemented with 5 mM EDTA, and analyzedon an LSR II flow cytometer (BD). A minimum of 100,000 events werecollected per sample. Mean fluorescent intensity was calculated using aminimum of 20,000 cells per sample using FlowJo software (Treestar), andrelative expression calculations and error propagation were conducted asdescribed for the microplate assays.

Statistical Analysis of Promoter Design Features.

In order to use computational analysis to compare promoter designs, itwas necessary to define quantitative descriptors, or features, thatcapture distinguishing architectural aspects of each promoter. Becauseour goal was to elucidate general design principles, we chose to limitour features to those describing the quantity and location of thevarious 9 bp ZFP binding sites. Following this approach, we defined 17features that describe the locations of ZFP binding sites relative toboth the −10 box (TATA box) and −35 region and relative positioningamongst the ZFP binding sites. In order to determine which promoterfeatures were important for explaining variation in performance betweenpromoters, several feature selection methods were applied to each set ofinput and output data (both of which were mean-centered andvariance-scaled) to generate rankings of feature importance, noting thatfeature independence was not assumed. For these analyses, featuresalways served as the regression inputs. The output was the“repressibility”, which we defined as the negative of relativeexpression, such that a promoter-ZFP combination with a highrepressibility exhibits low relative expression.

Three feature selection techniques were utilized: partial least squaresregression (PLSR), Random Forest, and Lasso. PLSR was executed using thebuilt-in MATLAB function, plsregress. To determine feature importance, apermutation test was used⁵⁸. Briefly, the output vector was randomlypermuted, and PLSR was executed for this meaningless output vector, suchthat when this process was repeated multiple times, we calculated thestandard deviation associated with each coefficient (one coefficient perfeature); thereby, the ratio of true coefficient magnitude to thestandard deviation associated with this coefficient provided a metric bywhich features can be ranked in order of importance. To implement theRandom Forest method, we modified a MATLAB script developed byJaiantilal (http://code.google.com/p/randoinforest-matlab/), which wasbased upon a method originally described by Breiman and Cutler(http://www.stat.berkeley.edu/˜breiman/RandomForests/). The last featureselection method used was Lasso regression, also known as sparse orregularized regression⁵⁹. Lasso feature selection is generallyconsidered more robust than a permutation test or Random Forest, becausethe selection is built into model generation and does not requireremoving features from a predictive model. Each of these methods isdescribed in full detail below.

Statistical Analysis of Promoter Design Features.

In order to apply computational methods to describe the library ofpromoters, it was necessary to choose quantitative descriptors, orfeatures, that describe architectural properties of each promoter. Wechose 17 features to describe the location of binding sites, relative tothe −10 and −35 boxes, and to other binding sites. Two assumptions areassociated with our choice of features. First, features were not assumedto be independent. Second, the expression of the reporter gene wasassumed to depend solely on the repression of a bound biosensor or zincfinger protein.

In order to determine which promoter features are important, threefeature selection methods were used. All three methods used the sameinput and output data to generate a ranking of feature importance. Alldata were mean-centered and variance-scaled before these methods wereapplied. Input data consisted of a matrix of promoter indices andfeature variables (62 promoter indices in rows, 17 feature variables incolumns). Output data are described above. The “repressibility” valuefor each promoter was defined as the negative relative expression of thereporter gene (GFP).

The following three feature selection techniques generated rankings ofthe features in order of importance.

Partial Least Squares Regression.

The first feature selection method used was a permutation test usingpartial least squares regression (PLSR). First, PLSR was executed usingthe built-in MATLAB function, plsregress. This function returns apredictive model for the output values through regression coefficientsfor each feature. To determine feature importance, a permutation testwas used (Janes et al., 2004). The output vector was randomly permuted,and PLSR was executed for the meaningless output vector. Regressioncoefficients were recorded for 1000 permutations, and a mean andstandard deviation was calculated for the coefficient for each feature.After many permutations, coefficient means approached zero, as isexpected for random permutations, but the standard deviations associatedwith each coefficient approached a different finite value for eachfeature, which indicates the degree to which that coefficient fluctuatesrandomly. Features for which the coefficients were greater in magnitudethan the random variance are likely to be more significant. Therefore,the ratio of coefficient magnitude to the standard deviation associatedwith each coefficient provided a metric by which features were ranked inorder of importance. In addition to rankings described in the main text(FIG. 3), coefficients and standard deviations for each feature arereported FIG. 10.

The MATLAB function, plsregress, also provides a vector of outputvariance explained by each feature. Summing these gives the overallvariance explained in the output data by the regression. PLSR wasexecuted with one feature removed, and the loss of output varianceexplained was recorded for each feature. It is important to note thatranking through this loss of output variance explained, or loss ofpredictive power, yields the same ranking as the permutation test. Usingthis ranking, PLSR was executed with an increasing number of features,in order of importance, with the output variance explained recorded eachtime. The output variance explained was also recorded for an increasingnumber of principal components used in the regression. These both wereplotted to show the contribution of each feature or principal componentto the regression model (FIG. 3B).

Random Forest.

The second feature selection method utilized was Random Forest. Toimplement the Random Forest method, we modified a MATLAB scriptdeveloped by Jaiantilal(https://code.google.com/p/randomforest-matlab/), which was based upon amethod originally described by Breiman and Cutler(http://www.stat.berkeley.edu/˜breiman/RandomForests/). First, thepromoter library was divided into 54 promoters selected randomly tocomprise a training set, leaving 8 promoters as a test set. Next, 6features were “bagged” into a subset by random selection withoutreplacement. The size of this subset is traditionally one third of thetotal set of features, which in this case rounds to 6 features(http://statweb.stanford.edu/˜tibs/ElemStatLearn/). Next, a subset ofpromoters from the training set was randomly selected with replacement.The size of this subset is similarly one third of the total number ofpromoters in the training set, yielding an 18 promoter subset. Adecision tree was then generated, its predictions were tested againstthe data from the test set of promoters, and the mean square error wasrecorded. This process was repeated for a large number of baggedpromoter training sets, while retaining the same subset of 6 features.This overall sequence was then repeated for a large number of featuresubsets, generating a total of 100 decision trees, each of which usedthe same test set. Finally, this entire process was repeated for 100different random choices of test set, generating a total of 10,000decision trees. To assess the importance of a feature, the input datawithin the test set were perturbed such that the feature valuesassociated with each promoter (e.g., number of ZFP binding sites) wererandomly permuted by shuffling. Any decision tree that included thefeature of interest was then retested using the perturbed input data.The increase in mean squared error (i.e., reduction in predictive power)was averaged over all trees containing this feature. This metric(average increase in mean square error) was thus used to generate aranking of features by importance, such that features with a greateraverage increase in mean square error were ranked as more important.(FIG. 10).

Lasso Regression.

The last feature selection method used was Lasso regression, also knownas sparse or regularized regression. This type of feature selection isgenerally considered more robust than a permutation test or randomforest, because the selection is built into the model generation, anddoes not require removing features from a predictive model (Tibshirani,R. (1996)). Lasso regression uses the least squares method, and isregularized by placing a constraint on the sum of the absolute value ofthe regression coefficients. Mathematically, the method places a penaltyon large coefficient magnitudes by minimizing the following expression:

${\sum\limits_{i = 1}^{N}( {y_{i} - {\sum\limits_{j}{\beta_{j}x_{ij}}}} )^{2}} + {\lambda{\sum\limits_{j}{\beta_{j}}}}$

In this expression, yi represents output data for the ith promoter, β isthe regression coefficient for the jth feature, and xij is input data(feature variable j for promoter i). The value of λ is a tunableparameter that determines the extent of regularization. With thismethod, coefficients of unimportant features shrink to zero as λ isincreased. Using the MATLAB function, lasso, Lasso regression wasexecuted for 100 increasing values of lambda, with the number offeatures with non-zero coefficients shrinking from 17 to 0. Eachregression iteration (corresponding to each value of λ) was tested using10-fold cross validation, and a mean squared error was recorded for eachiteration. For each feature, the number of regression iterations forwhich it had a non-zero coefficient was recorded. This metric was usedto generate a ranking of features in order of importance, with the mostimportant features having non-zero coefficients for larger values of A.In addition to the feature ranking, the mean squared error and number offeatures with non-zero coefficients were plotted together versus thevalue of λ (FIG. 3C).

Abbreviations

MBP—Maltose binding protein

ZFP—Zinc finger protein

TALE—Transcription activator-like effector

PLSR—Partial Least squares regression

MSE—Mean squared error

BLA—Beta-lactamase

SZF—Split zinc finger

SP—Split protein

IPTG—Isopropyl β-D-1-thiogalactopyranoside

LB—Lysogeny broth

OD—Optical density at 600 nm

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Example 2

Title: Development of Novel Metabolite-Responsive Transcription FactorsVia Transposon-Mediated Protein Fusion

Abstract

Naturally evolved metabolite-responsive biosensors enable applicationsin metabolic engineering, ranging from screening large genetic librariesto dynamically regulating biosynthetic pathways. However, there are manymetabolites for which a natural biosensor does not exist. To addressthis need, we developed a general method for convertingmetabolite-binding proteins into metabolite-responsive transcriptionfactors—Biosensor Engineering by Random Domain Insertion (BERDI). Thisapproach takes advantage of an unbiased in vitro transposon insertionreaction to generate all possible insertions of a DNA-binding domaininto a metabolite-binding protein, followed by fluorescence activatedcell sorting (FACS) to isolate functional biosensors. To develop andevaluate the BERDI method, we generated a library of candidatebiosensors in which a zinc finger DNA-binding domain was inserted intomaltose binding protein, which served as a model well-studied metabolitebinding protein. Library diversity was characterized by several methods,a selection scheme was deployed, and ultimately several distinct andfunctional maltose-responsive transcriptional biosensors wereidentified. The BERDI method comprises a generalizable strategy that mayultimately be applied to convert a wide range of metabolite-bindingproteins into novel biosensors for applications in metabolic engineeringand synthetic biology.

Introduction

Metabolite-responsive biosensors have a wide variety of uses, from basicresearch and discovery, to diagnostics, to engineered biosynthesis(Khalil and Collins, 2010). Such biosensors include diverse sensing andoutput modalities, including fluorescent and FRET-based biosensors(Golynskiy et al., 2011, Strianese et al., 2012), RNA-based biosensors(Kang et al., 2014, Michener et al., 2012), and transcription factorbiosensors (Brockman and Prather, 2015, Dietrich et al., 2010, Venayaket al., 2015). Transcription factor biosensors have proven to beespecially powerful for bioengineering, facilitating dynamic profilingof intracellular glucaric acid (Rogers et al., 2015) and malonyl-CoA (Liet al., 2015), and enabling high-throughput screening of large geneticlibraries constructed to achieve biosynthesis of 1-butantol, succinate,and adipate (Dietrich et al., 2013), benzoic acids (van Sint Fiet etal., 2006), and _(L)-Lysine (Binder et al., 2012). Moreover,transcription factor biosensors have been harnessed to implement dynamicintracellular feedback control, balancing metabolic fluxes to increaseproduction titers and yields of lycopene (Farmer and Liao, 2000), fattyacid ethyl ester (Zhang et al., 2012), amorphadiene (Dahl et al., 2013),1-butanol (Dietrich et al., 2013), and malonyl-CoA (Liu et al., 2015).Notably, these examples relied upon naturally-evolved transcriptionfactor biosensors, and broader utilization of such approaches iscurrently restricted by the limited pool of naturally evolved (andknown) metabolite-responsive biosensors. Thus, approaches for generatingnovel metabolite biosensors are required.

Several strategies for generating new biosensors have been explored. Oneapproach for generating new transcription factor biosensors is fusion ofthe ligand-binding domain from one transcription factor to theDNA-binding domain from a different transcription factor. However, thesechimeric biosensors are generally limited to fusions within families ofstructurally-related transcription factors, such as the LacI/GalRfamily, in order to preserve mechanisms of ligand-responsiveness thatarise from allosteric regulation (Meinhardt et al., 2012, Shis et al.,2014). An alternative approach is to fuse a metabolite-binding protein(which multimerizes upon ligand-binding) to a natural transcriptionfactor, such as AraC, to generate chimeras in which metabolite bindingmodulates transcription factor activity (Chou and Keasling, 2013).Additionally, the binding pockets of transcription factors such as LuxR(Collins et al., 2005, Collins et al., 2006), AraC (Tang and Cirino,2011, Tang et al., 2008, Tang et al., 2013), and XylR (Mohn et al.,2006) have been mutagenized and evolved to bind new, albeit structurallysimilar, ligands. In a recently reported strategy for engineering novelbiosensors in eukaryotes, fusion proteins were engineered to be unstablein the absence of ligand, such that the addition of the ligandstabilized the protein and enabled it to carry out its functional role(Feng et al., 2015). While these findings have generated both usefulbiosensors and novel insights into biosensor design, most methodsinclude some inherent limitation on the extent to which they may begeneralized to build biosensors for any metabolite of interest.Therefore, there remains an outstanding need for new methods forgenerating novel transcription factor biosensors.

We recently reported a new strategy for converting a ligand-bindingprotein into a transcription factor biosensor (Younger et al., 2016). Inthis proof-of-principle investigation, the Escherichia coli maltosebinding protein (MBP) was genetically fused with a modular zinc fingerDNA binding domain (ZFP) to generate a novel maltose-responsivetranscription factor, in which the addition of maltose alleviatedtranscriptional repression of an engineered promoter. This demonstrationleveraged a wealth of prior knowledge pertaining to MBP; specificallythe ZFP was inserted into MBP at a position that was previouslyidentified via random fusion between MBP and TEM1 β-lactamase (bla) togenerate a maltose-regulated bla (Guntas et al., 2004). Whether otherfusions between MBP and a ZFP could generate a functional biosensor, andwhether a functional biosensor could be generated in the absence of suchprior knowledge remained open questions.

To address these questions, here we developed an efficient method forgenerating combinatorial fusions between a ligand-binding protein and aZFP, followed by isolation of functional biosensors from this diverselibrary, which we term Biosensor Engineering by Random Domain Insertion(BERDI). To develop and validate this method, a library of fusionsbetween a ZFP and MBP was generated, analyzed, and screened. Notably, wesuccessfully identified three novel maltose-responsive biosensors, whichvalidates this overall approach. Thus, BERDI comprises a generalizablestrategy that may ultimately be applied to convert a wide range ofmetabolite-binding proteins into novel biosensors.

Materials and Methods

Bacterial Strains and Culturing.

All experiments were conducted in DS941 Z1 Escherichia coli cells(AB1157, recF143, lacI^(q) lacZ ΔM15, P_(laciq)-LacI, P_(N25)-TetR).Cells were maintained in Lysogeny Broth (LB) Lennox formulation (10 g/Ltryptone, 5 g/L yeast extract, 5 g/L NaCl) supplemented with appropriateantibiotics (Ampicillin 100 μg/mL, Kanamycin 50 μg/mL, and/orChloramphenicol 34 μg/mL). All experimental analyses were conducted inM9 minimal media (1×M9 salts, 0.2% Cas amino acids, 2 mM MgSO₄, 0.1 mMCaCl₂, 1 mM Thiamine HCl) containing glycerol (0.4%) as the primarycarbon source. Variable amounts of isopropyl β-D-1-thiogalactopyranoside(IPTG) were added, as indicated, to induce biosensor expression. Maltosemonohydrate was added to the media at a final concentration of 100 mM,where indicated.

The biosensor expression vector was built using standard molecularbiology techniques using parts (GFPmut3b and pTrc2) gifted by JimCollins (MIT) (Litcofsky et al., 2012). The green fluorescent protein(GFP) reporter plasmid driven by the pGo92 zinc finger-responsivepromoter was previously described (Younger, Dalvie, Rottinghaus andLeonard, 2016). Custom RBS sequences for the biosensor and reporterplasmids were designed using the RBS calculator (Salis et al., 2009).The camR and sacB ORFs were transferred from pKM154, which was gifted byKenan Murphy (University of Massachusetts) (Murphy et al., 2000)(Addgene plasmid #13036), into a storage vector containing MuAtransposon recognition sequences, flanked by BglII restriction sites(pAY438). The BCR-ABL1 ZFP was subcloned into a storage vector flankedby NotI restriction sites (pAY437). Description of all plasmids used inthis study can be found in in Table 1 below.

TABLE 1 Plasmids used in this study Plasmid name Description ResistanceOrigin Reference pAY430 pGo92-GFP. Zinc finger repressible Amp^(R) pA15Younger et al., promoter driving GFP. 2016 pAY447 MBP (no promoter)Amp^(R) ColE1 This work pAY438 BglII and transposon recognition Kan^(R)Cm^(R) ColE1 This work sequences flanking CmR and SacB pAY437 BCR-ABL1zinc finger, without a stop Amp^(R) ColE1 This work codon or promoter,flanked by NotI sites pAY431 pTrc2 promoter and RBS followed by Kan^(R)ColE1 This work KpnI and SphI MCS, followed by a terminator pAY419 316Rreference biosensor Kan^(R) ColE1 Younger et al., (SP from Younger etal. 2016) 2016 pAY470 277A biosensor (in pAY431) Kan^(R) ColE1 This workpAY451 270A double ZFP biosensor Kan^(R) ColE1 This work (in pAY431)pAY453 270A single biosensor (in pAY431) Kan^(R) ColE1 This work pAY452335P (3AA) biosensor (in pAY431) Kan^(R) ColE1 This work pAY469 335P(2AA) biosensor (in pAY431) Kan^(R) ColE1 This work pAY468 335P (1AA)biosensor (in pAY431) Kan^(R) ColE1 This work pAY460 335P (0AA)biosensor (in pAY431) Kan^(R) ColE1 This work

Candidate Biosensor Library Construction.

The MuA transposase inserts its transposon randomly, and in either aforward or reverse direction, into any DNA sequence (Haapa et al.,1999). Furthermore, the transposon can be inserted in any of the 3possible codon frames in MBP. A detailed description of the transposonsequence and potential scar options can be found in FIG. 20.

First, a library representing all possible random insertions within MBPwas generated. Double-stranded DNA comprising a transpo son conferringchloramphenicol resistance as well as containing the sacB gene fornegative selection with sucrose was digested out of a storage plasmid(pAY438) using BglII, gel extracted, and cleaned by ethanolprecipitation/resuspension in 40 μL of TE buffer. In vitro transpositionreactions were carried out using the Mutation Generation System kit(Thermo Scientific # F701), as per the manufacturer's protocol. Briefly,100 ng of purified transposon was mixed with 200 ng of target plasmidencoding MBP (pAY447), and the mixture was incubated with 1 μL of 0.22ng/μL MuA transposase for 4 h at 30° C. MuA was heat-inactivated (10 minat 75° C.), and a PCR cleanup (IBI Scientific) was conducted to recoverthe library. The entire library was electroporated into two tubes ofelectrically-competent E. coli cells (˜250 μL final volume each).Transformed cells were selected on plates containing chloramphenicol(transposon) as well as ampicillin (plasmid backbone). Serial dilutionswere made at each cloning step and extrapolated to estimate librarysize. The MBP gene was digested out with restriction enzymes KpnI andSphI and purified by agarose gel electrophoresis to separate the bandrepresenting MBP with transposon insertion (3923 bp) from the bandrepresenting WT MBP (1122 bp). The MBP with transposon band was purifiedand cloned into an expression plasmid under the control of alac-inducible promoter pTrc2 (pAY431). Finally, restriction digestion(using the NotI site present in the transposon scar) was used to replacethe transposon with the sequence encoding the ZFP (BCR-ABL1), and thisligation was transformed into competent E. coli cells that alreadycontained the ZFP-responsive GFP reporter plasmid (pAY430). Cells wereselected with ampicillin and kanamycin for both plasmids as well as 10%sucrose to maximize loss of the transposon, yielding the naïve(unselected) candidate biosensor library.

Microplate-Based Fluorescent Assays and Analysis.

Cultures were inoculated from single colonies into 2 mL of M9 media andgrown overnight to stationary phase. Overnight cultures were diluted1:10 and grown for 1-2 h (OD600 ˜0.5). Cultures were again diluted 1:10(OD600 ˜0.05), plated in black-walled clear bottom 96-well plates inbiological triplicate, and induced with 30 μM IPTG and/or 100 mMmaltose. Plates with lids were incubated and shaken in a continuousdouble orbital pattern at 548 cpm (2 mm) inside a BioTek Synergy H1plate reader for 10 h with GFP fluorescence and OD600 absorptionmeasurements taken every 15 min. Monochrometer settings were 485/515 nmfor GFP.

Flow Cytometry and Fluorescence Activated Cell Sorting (FACS).

Overnight cultures (2 mL) were diluted 1:10 into a fresh 2 mL aliquot ofM9 media and grown for 1-2 hours (OD600˜0.5). Cultures were againdiluted 1:10 (OD600˜0.05) in a fresh 2 mL of either M9 media, or M9media containing 100 μM IPTG. Cultures were grown for 4 h post-inductionprior to FACS sorting. Cells were then diluted down to a concentrationof 10⁷ cells/mL in 4° PBS. Sorting was performed on a BD FACS Aria IIinstrument (BD Biosciences, San Jose, USA) using an 85 μm tip with a 488nm excitation laser and a FITC emission filter (530/30 nm). This FITCchannel was used for analysis of GFP expression. Cells were first gatedbased upon forward and side scatter, then the population of single cellswere plotted on a GFP histogram. To set a gate for recovering cellsexhibiting biosensor-mediated repression of reporter (GFP) output, adistribution of GFP fluorescence in cells was obtained from thepopulation (100,000 events), and gating was set such that no more than1% of this “ON” (uninduced) population would be recovered. This gate wasused to recover biosensor candidates capable of reporter repression. Torecover “reversible” repressors (minimize false positive repressors),the same gating definition described above was used, but this time (inthe absence of IPTG), “ON” cells were recovered. For each round ofsorting, 100,000 cells were recovered into 3 mL of M9 minimal mediacontaining ampicillin and kanamycin, and this culture was subsequentlyinoculated into 50 mL of M9 containing ampicillin and kanamycin andgrown overnight at 37° C. Subsequent sorts were performed (as indicated)the next day using the sorted and expanded population, as above.Traditional flow cytometry was performed on a LSRII flow cytometer (BDBiosciences, San Jose, USA). For all flow cytometry analyses, meanfluorescent intensity was calculated based on the GFP histograms ofsingle cells (gated by forward and side scatter) using FlowJo Software(Tree Star).

Digest-Based Evaluation of Library Diversity.

All gel electrophoresis experiments were conducted with a 1% agarose geland run in 1×TAE (tris acetate EDTA) at 120 volts. DNA was stained usingSYBR Safe (Thermo Scientific) and imaged under blue light. Band sizeswere estimated using a 1 kb ladder (New England BioLabs). Exposure timewas adjusted to maximize the differences between the lows and highs inthe gel. Plot profiles of resulting gel images were analyzed usingImager s “plot profile” function. Intensity profiles for each lane weregenerated by subtracting the “gray value” from an empty gel lane fromthe grey value evaluated along the length of the lane of interest.

Next-Generation Sequencing (NGS) and Analysis.

The naïve library of candidate biosensors was digested using KpnI andSphI, and this DNA fragment was gel extracted and subjected to probesonication on ice (QSonica Q700 probe sonicator; 45 minutes of 30seconds on, 15 seconds off at 10% maximum intensity) to shear thelibrary into fragments less than 500 bp in length. Library preparationwas done by PCR amplifying four equally sized regions of MBP, usingprimers incorporating common sequences derived from Fluidigm'sAccessArray system (Moonsamy et al., 2013) to be used for NGS. The PCRprimers were designed to bind either the MBP or ZFP sequence, and an MBPprimer was always paired with a ZFP primer for library preparation, suchthat each amplicon would contain one MBP-ZFP boundary, and thus the ZFPinsertion site could be determined. In total, eight unique PCR reactionswere run on the naïve library: four evenly spaced MBP primers, eachpaired with either the forward or reverse ZFP primer. See FIG. 21 for avisual layout of the primers and Table 2 for a list of PCR reactions andprimer sequences. Two biological replicates of the library were preparedfor a total of sixteen reactions. Samples were submitted to theUniversity of Illinois at Chicago (UIC) Sequencing Core, where Illuminaadapters and custom barcodes were further appended to the amplicons viaanother PCR reaction. All samples were run together on an Illumina MiSeqlane using paired end DNA sequencing. In total, ˜8M reads were generatedfrom the sixteen samples.

TABLE 2 PCR primer pairs for NGS. CS - Common sequence Forward primerReverse primer PCR (common sequence #) (common sequence #) ZFP NumberPrimer 5′-3′ Primer 5′-3′ direction 1 AYP828 (CS1) AYP829 (CS2) Forwardacactgacgacatggttctacacaacgatt tacggtagcagagacttggtctctagggctcatacatagctaaaaggtacc(SEQ ID NO:  agctctagccat (SEQ ID NO: 83) 79) 2AYP830 (CS1) AYP829 (CS2) Forward acactgacgacatggttctacaggcaagctacggtagcagagacttggtctctagggct tgattgcttaccc (SEQ ID NO: 80)agctctagccat (SEQ ID NO: 83) 3 AYP831 (CS1) AYP833 (CS2) Forwardacactgacgacatggttctacaacaggcg tacggtagcagagacttggtctgccagctctagaagggatcc (SEQ ID NO: 81) ttgttcggac (SEQ ID NO: 84) 4 AYP831 (CS1)AYP832 (CS2) Forward acactgacgacatggttctacaacaggcgtacggtagcagagacttggtctgggtgttca agaagggatcc (SEQ ID NO: 81)ataattgggcatgc (SEQ ID NO: 85) 5 AYP828 (CS1) AYP835 (CS2) Reverseacactgacgacatggttctacacaacgatt tacggtagcagagacttggtctacaggcgacatacatagctaaaaggtacc gaagggatcc (SEQ ID NO: 86) (SEQ ID NO: 79) 6AYP830 (CS1) AYP835 (CS2) Reverse acactgacgacatggttctacaggcaagctacggtagcagagacttggtctacaggcga tgattgcttaccc (SEQ ID NO: 80)gaagggatcc (SEQ ID NO: 86) 7 AYP834 (CS1) AYP833 (CS2) Reverseacactgacgacatggttctacatctagggc tacggtagcagagacttggtctgccagctcttagctctagccat (SEQ ID NO: 82) ttgttcggac (SEQ ID NO: 84) 8 AYP834 (CS1)AYP832 (CS2) Reverse acactgacgacatggttctacatctagggctacggtagcagagacttggtctgggtgttca tagctctagccat (SEQ ID NO: 82)ataattgggcatgc (SEQ ID NO: 85)

All data analysis was performed using customized software (written inPython). Briefly, reads were first filtered to retain only those readscontaining the transposon scar sequence as well as the sequences of theprimers used to generate the amplicon corresponding to that sample.Next, we identified viable paired end reads as those in which at least12 contiguous bases at the end of one read perfectly matched the reversecomplement of its paired read. Only such viable paired end reads werecarried forward. Next, to discard low-quality reads, we filtered outreads with Phred quality scores below 20 (a score of 20 corresponds to99% confidence in the identity of that base). Next, reads werepartitioned into bins by read length, in roughly 50 bp increments, toenable subsequent normalization of scores by read length. Reads werethen aligned to the MBP template sequence using the Needleman-Wunschalgorithm (using a gap opening penalty of 10.0, gap extension penalty of0.5, and the EDNAFULL scoring matrix). The resulting alignment scoreswere normalized by read length (i.e., by bin), since scores generallyincrease with length of alignment. Alignments that generally had lessthan 2 gaps per 50 bases aligned were carried forward. The resultingalignments were then analyzed to identify ZFP insertion sites, which wedefined as the first or last base to align to MBP, depending on theorientation of the amplicon being analyzed. Insertion sites were onlyclassified as “identified” if the alignment comprised a block of perfectalignment with MBP between the identified insertion site and the end ofthe read (i.e., reads were discarded if the alignment generated gaps ormismatches within the block of sequence where the read aligned to theMBP template). A graphical representation of this pipeline is presentedin FIG. 22. In all cases, the insertion site refers to the last base ofMBP upstream (5′) of the transposon insertion. All of the NGS analysiscode and data is available athttps://github.com/PeterSu92/BERDI-NGS-insertion-analysis.

Results

Generation of Random Domain Insertion Libraries Via TransposonMutagenesis.

Here we sought to develop an efficient method for generating noveltranscription factor biosensors, which we term Biosensor Engineering byRandom Domain Insertion (BERDI). The overall BERDI strategy issummarized in FIG. 15. This method utilizes the MuA transposase toinsert a transposon nonspecifically into a plasmid encoding the genesequence for the metabolite binding protein. Transposon mutagenesisprovides a simple and efficient method for generating a library ofinsertions due to its nonspecific and single insertion into each targetDNA molecule, minimal scar sequence, and ability to generate >10⁵variants in a single pot in vitro reaction (Haapa et al., 1999, Mehta etal., 2012, Nadler et al. 2016). The transposon is later exchanged for aZFP coding sequence to generate a library of candidate biosensors.Transposase insertion has been used to successfully circularly permuteproteins as well as to profile proteins for permissible insertion points(Edwards et al., 2008, Mehta et al., 2012, Nadler et al., 2016, Oakes etal., 2016, Segall-Shapiro et al., 2011). Here, we investigated whetherthis technique can be used to generate novel biosensors by randomlyinserting a ZFP into a ligand-binding protein. We reasoned that choosingMBP as a model ligand-binding domain would be the most effectivestrategy for evaluating the BERDI method, since (a) we know that atleast one such feasible biosensor should exist, based upon our priorwork (Younger et al., 2016), and (b) such an approach enabled us toutilize established reporter constructs and biosensor evaluationmethods.

First, a library of candidate biosensors was generated. MuA insertsrandomly into target DNA molecules (in either forward or reversedirection), such that for a plasmid of length n bases, the total numberof possible insertions is 2n. Additionally, multiplying the three framesin which the transposon can insert by the two directions in which thetransposon can insert (i.e., forward or reverse) yields six possibleinsertions for a given codon of target DNA. However, only one of thesesix insertions (forward and in-frame) will generate a productiveinsertion. The transposase also leaves a partially controllable scar(i.e., one has some choice in the design of this scar sequence).Therefore, we designed the transposon such that when the ZFP is insertedin frame with the rest of MBP, the resulting scars encode for linkerscomprising three alanine residues on either side of the ZFP domain (seeFIG. 20B for details).

Given that transposon insertions are random and independent of oneanother, we aimed to achieve a library size at least 10× greater thanthe maximum possible number of insertions—6,288 (the number ofdirections in which the transposon can insert, 2, multiplied by the sizeof the target plasmid, 3144 bp); this yields a target library size of62,880 members. Our initial transposition library generated over 8×10⁵colonies, or ˜125× the maximum library diversity. Next, the library wassubcloned to eliminate MBP variants lacking a transposon (i.e. when thetransposon inserted elsewhere in the plasmid backbone), and then thetransposon was replaced with a sequence encoding the ZFP to yield anaïve candidate biosensor library. At each step, we confirmed thatlibrary diversity exceeded the target of 10× oversampling (Table 3).

TABLE 3 Targeted and experimental library sizes Targeted Experi- libraryExperi- mental Theoretical size (10x mental Experimental Step librarysize oversampling) Results Oversampling Generate 3144 (bp MBP 62,880~800,000 ~125x  library plasmid) × 2 (forward or reverse) = 6,288 Clone1116 (bp of 22,320 ~30,000 ~14x transposed MBP) × 2 gene into (forwardor expression reverse) = plasmid 2,232 Exchange 1116 (bp of 22,320~30,000 ~14x transposon MBP) × 2 for ZFP (forward or reverse) = 2,232

Analyzing Diversity of the Naïve Library.

In order to evaluate library diversity prior to sorting for functionalbiosensors, we analyzed the naïve library using three distinct methods.First, sequences from the naïve library were digested out of theexpression vector library, and these biosensor-encoding fragments werethen subsequently digested with a restriction enzyme recognizing aunique site in the sequence encoding the ZFP. These digests wereevaluated by gel electrophoresis (FIG. 16A,B), which yielded a smearconsistent with the presence of many diverse insertions. We nextperformed both Sanger sequencing on 46 individual colonies andnext-generation sequencing (NGS) on the library, by PCR amplifyingregions containing both the MBP and ZFP and then subjecting theseamplicons to NGS (FIG. 21). Confirmed insertions were identified bysequence analysis (FIG. 16C). Colony sequencing was performed tocomplement NGS, since the latter is expected to be impacted by biasesintroduced during PCR-based library preparation. A full list ofinsertion counts identified by sequencing individual colonies and NGScan be found in Table 4.

TABLE 4 List of all insertions found by NGS and Sanger (colony)sequencing Insertion Count by Count by Insertion Count by Count byInsertion Count by Count by Position NGS Sanger Position NGS SangerPosition NGS Sanger 1 7 0 384 2 0 771 1 0 2 35358 0 391 2 0 779 1 0 9 21 394 3 0 785 5 0 10 1 0 404 1 0 786 662975 0 12 1 0 405 1 0 787 666 013 1 0 410 2 0 788 1 0 14 13 0 412 2 0 789 1 0 15 868175 1 417 1 0 791 40 16 4 0 422 1 0 794 1 0 18 1 0 423 1 0 807 66 0 32 2 0 442 1 0 808 2 040 1 0 443 66 1 809 262424 0 59 4 0 444 318427 0 810 192 0 60 24625 0445 1 0 812 6 0 74 2 0 448 1 0 814 2 0 152 15 0 494 1 0 867 2 4 201 42 0550 1 0 870 21 0 285 1 0 561 1 0 896 0 1 286 2708 4 563 1 0 897 0 1 2872 0 564 7093 4 898 0 1 300 3366 0 565 130462 0 899 2840 0 306 0 1 566 40 900 7824 2 325 12 0 570 1 0 901 41428 15 326 4 0 580 2299 0 902 1 0329 1 0 581 8766 0 903 44428 5 330 10 0 594 27159 2 904 3 0 331 4 0 59568187 0 907 1 0 332 3 0 614 2642 0 938 2 0 333 3 0 615 2269 0 953 1 0334 1 0 616 1 0 976 4 0 336 3 0 623 8349 0 978 32589 0 337 2 0 637 399571 979 14 0 340 8 0 638 5345 0 987 3932 0 341 1 0 639 1 0 993 1 0 346 1 0680 8378 0 1004 3219 0 348 1 0 681 118 0 1068 595 0 349 5 0 670 0 1 10801 0 350 7 0 706 1 0 1086 72 0 351 2 0 711 201312 0 1087 183665 1 354 4 0712 1659 0 1088 3 0 360 1 0 713 8 0 1090 2 0 361 2 0 715 1 0 1099 1 0363 3 0 716 1 0 1102 1 0 364 1 0 717 1 0 1103 1 0 365 2 0 719 1 0 1104 10 366 14 0 723 1 0 1105 1 0 367 3 0 748 1 0 1110 1 0 371 1 0 757 1 01111 4 0 373 16 0 758 181930 0 1112 236842 0 382 8 0 759 884 0 1113 1130 383 3 0 762 5 0 1115 1 0

When using conservative NGS analysis parameters (e.g., utilizing onlyalignments wherein the read perfectly matched the template, withoutgaps) to identify insertions, we observed at least 148 insertions acrossMBP, representing 13.2% of all possible insertions. Out of these, 47(31%) were in frame, and the percentage of insertions containing aforward-facing ZFP was 48%, which is consistent with the expectationthat MuA-mediated insertion is random (Table 5). We identified 37productive insertions (forward-facing and in-frame) from these analyses.

TABLE 5 Frequency of insertion types observed in the naïve candidatebiosensor library Observed by colony (Sanger) Type of Expected ifObserved by NGS sequencing insertion unbiased (n = 148 insertions) (n =17 insertions) In frame 33% 30% 12% Out of frame 66% 70% 88% Forward ZFP50% 49% 48% Reverse ZFP 50% 51% 52%

To evaluate the bias that occurred during the PCR amplificationperformed in preparation for NGS, we analyzed several parameters. In ourNGS data analysis, we observed insertion counts ranging from veryabundant (10⁶ counts) to very rare (1 count), and the top five mostabundant insertions encompassed 64.8% of all insertion counts. 12insertions out of the 46 found by Sanger sequencing individual colonies(counting both productive and non-productive insertions) were alsoidentified in the NGS analysis, yet only one insertion site identifiedby colony sequencing matched any of the top five most frequentinsertions identified by NGS analysis. Also, five of the insertion sitesidentified by sequencing colonies represented insertions not found byNGS. We therefore concluded that amplification bias significantlyaffected our NGS results, which suggests that our naïve librarydiversity was broader than the limited set of insertions confirmed byour conservative NGS analysis. Moreover, since the transposon insertionratios (forward/reverse, in-frame vs. out of frame) demonstrated thatthe transposase functioned as expected (Table 5), and since our librarysize exceeded 10× oversampling, we concluded that the library likelycontained sufficient diversity and we proceeded to investigate whetherthe library contained any functional bio sensors.

Identification of Functional Biosensors.

Our overall strategy was to first enrich for candidate biosensorscapable of repressing the reporter in the absence of maltose, then toreselect to eliminate false positives, and then to identifymaltose-responsive biosensors from within that pool (FIG. 17A). Thenaïve library was first screened by FACS to enrich for candidatebiosensor clones which, when expressed at a high level (100 μM IPTGinduction), repressed the GFP reporter output. The selected pool wasrecovered and regrown, and this process was iterated on threeconsecutive days to enrich for candidate biosensors that were at leastcapable of repressing reporter output. Next, this population ofcandidate biosensors was re-screened by FACS in the absence of IPTG, torecover “reversible” repressors (clones which express GFP in the absenceof IPTG) in order to minimize false positives. Following these fourrounds of screening, the recovered cells were plated and clonallyassayed for maltose-responsiveness. Candidate biosensors were initiallyevaluated under conditions of 30 μM IPTG to induce the expression of thebiosensor and 100 mM maltose, which are conditions under which wepreviously observed maximal maltose-responsiveness of the referencebiosensor (Younger et al., 2016). At this point, library membersexhibiting both (a) at least a 2-fold repression of reporter output inthe absence of maltose and (b) any significant maltose responsive weredefined as “functional biosensors” and were sequenced. Of the 672colonies analyzed, 340 demonstrated the ability to repress the reporter,but only 159 of those met both criteria for potential biosensors. These159 colonies represented three unique biosensors (270A, 277A, and 335P).The other 181 repressors represented out-of-frame insertions at sixpositions (5E, 31G, 188G, 194T, 213I, and 262V). Moreover, none of thethree novel biosensors identified were detected in the original naïvelibrary, via either colony (Sanger) sequencing or NGS. Together, theseobservations indicate that our strategy substantially enriched forfunctional biosensor constructs, even if they were rather rare in theoriginal naïve library.

The three functional biosensors, representing a ZFP insertion at 277A,an insertion of two ZFPs at 270A, and a single ZFP insertion at 335P,were next examined in greater detail. These insertions are depictedgraphically in FIG. 17B, along with the position at which the ZFP waspreviously inserted by design in the reference biosensor, at position316R (Younger et al., 2016). Interestingly, these four insertion pointsare distributed in three distinct regions of MBP. All four are on theoutside of the protein and are either in a loop (270A) or at the end ofan α-helix, near a loop (316R, 277A, and 335P). Given the sample size,and the lack of crystal structures of the new biosensors, it is not yetpossible to predict whether they share other features that lendthemselves to maltose-responsive transcriptional regulation. Performanceof the new biosensors was next compared to that achieved by thereference biosensor (FIG. 17C). Both the 277A and 270A biosensorsexhibited similar repression to that conferred by the referencebiosensor (˜3 to 4-fold), however, neither 277A nor 270A exhibited asmuch maltose-responsiveness as did the reference biosensor. Notably, the335P biosensor exhibited much better repression (˜10-fold) compared tothe other three constructs, while also exhibiting substantialresponsiveness to maltose. We next investigated why our BERDI methodrecovered a 270A double ZFP insertion but not a 270A single ZFPinsertion. Such a double ZFP insertion is indeed a potential productthat could be generated when cloning the ZFP cassette in place of thetransposon cassette (both ends of the ZFP cassette use the same NotIrestriction site), but given the ligation ratios used, we expected sucha double insertion to be substantially less frequent than a singleinsertion. Therefore, we next generated the 270A single ZFP insertionbiosensor to evaluate its performance. First, the 270A single ZFPconstruct exhibited milder repression compared to the 270A double ZFPvariant (FIG. 23), which would explain why the single insertion variantwas not (or was less) enriched during the initial three rounds of FACSscreening. Furthermore, the single ZFP insertion at 270A exhibited lessresponsiveness to maltose than did the double insertion biosensorvariant. Taken together, these clonal observations support theconclusion that the BERDI method generated and selected for functionalbiosensors, based upon their performance, as intended.

Biosensor Performance Characteristics: Dose and Linker Analysis.

Having identified several novel functional biosensors, we next evaluatedtheir performance characteristics. First, to investigate the impact ofbiosensor dose on reporter output repression and maltose sensitivity,the strongest repressor, 335P, was induced at a range of IPTGconcentrations (FIG. 18). At the highest IPTG concentration evaluatedhere (60 μM), the repression was not significantly greater than thatobserved at 25 or 30 μM IPTG, indicating that at these lowerconcentrations, saturating levels of the biosensor already achievemaximal repression. However, at 60 μM IPTG, the system was not sensitiveto maltose, indicating that the biosensor was in excess relative tointracellular maltose levels (which would be lower than extracellularmaltose levels). As the level of IPTG (and thus biosensor expression)decreased, the sensitivity of the 335P biosensor to maltose increased,but once the IPTG level dropped below 20 μM IPTG, the overall repressionof the reporter decreases, as expected given the decrease in biosensorprotein. Thus, there exists an optimal window of biosensor expressionthat confers maximal maltose sensitivity while maintaining sufficientpromoter repression (in the absence of maltose), corresponding to around25 μM IPTG (different from the 100 μM IPTG used during selection). Eachof these phenomena is consistent with observations previouslycharacterized with the reference biosensor (Younger et al., 2016),suggesting that at least at this functional level, the referencebiosensor and this novel BERDI-generated biosensor exhibit qualitativelysimilar performance characteristics.

Intriguingly, however, a biosensor matching the original referencebiosensor (e.g., a ZFP insertion 316R) was not recovered by the BERDImethod, and we next investigated why this may be. One possibleexplanation is that the linkers introduced via the BERDI method differfrom those included in the reference biosensor. The reference biosensorhas two amino linkers on either side of the ZFP—lysine and leucine onthe 5′ end of the ZFP insertion and an asparagine and valine on the 3′end—whereas the BERDI method introduces three alanines on either side ofthe ZFP (FIG. 20). To investigate the impact of this difference inlinkers, a biosensor was generated in which the ZFP was inserted at316R, with three alanines on each side of the ZFP, and this constructwas compared to the original reference biosensor (FIG. 19A).Surprisingly, the 316R biosensor with 3 Ala-linkers (mimicking thetransposon scar) completely lost its ability to repress the GFPreporter. This observation could explain why the BERDI method did notrecover a single ZFP insertion at 316R, and moreover, it demonstratesthe importance of linker length (and potentially composition) onimpacting overall biosensor performance. To further investigate howlinker length may affect biosensor performance, three variants of the335P biosensor (with 3AA linkers) were generated, with reduced linkerlengths (0-2 AA on each side of the ZFP). As the linker lengthshortened, the repression of the biosensor decreased (FIGS. 17B-C).Interestingly, the maltose-responsiveness also varied with linkerlength, such that the 1AA 335P biosensor exhibited the best combinationof promoter repression and maltose responsiveness. Altogether, theseobservations both validate the BERDI method for generating novelfunctional biosensors and indicate that this modular biosensor design isamenable to further refinement of biosensor performance.

Discussion

In this study, we developed and implemented the BERDI method for thegeneration of maltose-responsive MBP-ZFP fusion proteins in a rapid andefficient manner to find three new biosensors. The fact that multipleinsertions produced a bi-functional protein is not surprising given thata previous study found twelve functional insertions for a circularpermuted GFP into MBP using a similar method (Nadler, Morgan, Flamholz,Kortright and Savage, 2016). Additionally, another transposon insertionstudy demonstrated multiple bi-functional insertions of a cytochromeinto β-lactamase (Edwards, Busse, Allemann and Jones, 2008),demonstrating that if multiple possible insertions exist, this method iscapable of identifying them. A possible explanation for the tolerance ofthe proteins studied both here and in previous research is that manycircularly permuted proteins are able to retain their function,demonstrated in a study that found 15 unique functional circularpermutations of an adenylate kinase using transposon mutagenesis (Mehta,Liu and Silberg, 2012). A second explanation may be that these aremonomeric proteins, as homodimers would require the protein complex totolerate two changes simultaneously. These findings emphasize the needfor library based approaches, like the one described here, given thepropensity for a given protein to have multiple positions wherefunctional fusions can be created.

Despite some indication that our library did not sample the entireinsertion space due to limitations such as amplification bias, we foundthree novel functional biosensors in our library, and six variants thatwere out-of-frame, but that still exhibited mild (less than 2-fold)inducible repression (5E, 31G, 188G, 194T, 213I, and 262V). The threebiosensors that were enriched in the screening process were not detectedby either colony (Sanger) sequencing or NGS, indicating that ourscreening method can isolate infrequent mutants from the initiallibrary. As for the out-of-frame “repressors”, we hypothesize that thisis due to non-specific translation, since the start codon of the ZFPremained in the final constructs. Therefore, it is possible thatribosomes still translated the full ZFP along with the downstreamportion of MBP (out of frame), leading to a functional repressor. Thus,in future implementations of the BERDI method, it may be desirable toremove the start codon from the ZFP prior to library construction tominimize this issue and further enrich for productive biosensors overthese false positives. In any event, this phenomenon did not precludeour identification of functional biosensors, but rather it necessitatedclonal analysis of more candidate biosensors in the final step of theselection. Should the problem of false positive repressors proveintractable, an alternative solution would be to add a FACS-based screento enrich for ligand-responsive biosensors prior to clonal analysis.

One of our newly discovered biosensors (270A) had a double ZFPinsertion, which outperformed a similar biosensor comprising a singleZFP insertion at this position. It is possible that the presence of twoZFP domains, if correctly folded, increased repression due to the higherpotential conformational shift. While this phenomenon is neitherproblematic or advantageous, it is inherently tied to our use of aunique restriction site within the transposon recognition sequencesduring library generation, such that it seems reasonable to accept thisrare occurrence as a possible event that may occur during librarycreation. If this phenomenon were to prove problematic, librarygeneration could be performed with a greater ratio of backbone plasmidto ZFP cassette during the applicable ligation step. Alternatively,mutagenesis of the transposon recognition sequences might reveal analternative method that removes this possibility, although such a studyis outside the scope of this investigation, and our results suggest thatsuch a modification of the library generation method is not necessary.

Biosensor dose is critical when evaluating biosensor performance.Increasing biosensor expression increases the repression of thereporter, however it also limits the sensitivity to maltose. It ispossible that the binding of maltose does not ever completely ablate theability of the biosensor to bind DNA, therefore, regardless ofintracellular maltose concentrations, the reporter may never becompletely unbound by biosensors. This performance characteristic islikely to be unique for every biosensor created, which can becharacterized by a dose response analysis of both biosensor and ligandto tune desired biosensor properties.

Because protein structure and functional are so often intertwined,linker composition is vital to biosensor performance. The referencebiosensor was not found in the transposon based screen due to thedifferences in the linkers. However, three novel biosensors were found.This implies that not only does linker composition matter, but that ifwe had chosen different linkers in the transposon design, we likelystill would have found biosensors, albeit potentially at entirelydifferent sites. We hypothesized that using too long of a linker wouldreduce the degree to which ligand binding induces conformational changesthat are translated through the protein (e.g., via an allostericmechanism) to impair DNA binding. Conversely, using no linker mayprevent the ZFP from folding in a conformation conferring DNA binding inthe absence of ligand. Therefore, we hypothesized that designing ourlibrary to include three alanine linkers on each side of the ZFP wouldpotentially provide an inert and flexible linker that balances theseeffects. Indeed, the performance of the linker variants of the 335Pbiosensor largely supported this understanding of biosensor function;shortening linkers reduced biosensor-mediated repression and changed themaltose responsiveness, potentially indicating that the ZFP bound DNA toa lesser extent. Furthermore, the reference biosensor bound DNA when itincluded lysine and leucine residues flanking the N-terminus of the ZFPand asparagine and valine flanking the C-terminus of the ZFP, but whenthese linkers were each replaced with three alanines, promoterrepression was ablated, implying that both length and composition of thelinkers are important for performance. As is the case with biosensorexpression levels, the specific linkers are likely to impact everybiosensor differently. Importantly, this phenomenon may also provide anadditional handle for tuning biosensor performance. Although we did notinvestigate this possibility in this study, systematically varyinglinker sequence and length, as a perturbation to a candidate biosensor,may indeed confer improved performance for some biosensors.

The 335P biosensor has the performance characteristics that make itcapable of distinguishing between a high and low state of maltose thatcould be utilized for high-throughput screening or feedback controlmechanisms. However, the biosensors found by the BERDI method may notalways have the performance characteristic desired for a particularapplication. Therefore, using the BERDI method as a starting point togenerate functional biosensors, it may be possible to improve anybiosensor by saturation mutagenesis on the three alanine linkers, oreven the whole protein, followed by additional rounds of sorting toenrich for different performance characteristics.

The three novel biosensors described here were all found by sorting forinducible repressors, then clonal examination for maltoseresponsiveness. However, if ligand-responsive biosensors prove to beexceptionally rare, it could be useful to use FACS to enrich forligand-responsive biosensors, prior to clonal analysis, as noted above.Additionally, instead of using GFP and FACS as the screening system forligand responsive biosensor, GFP could be replaced with a geneconferring a survival selection. For example, if the reporter droveexpression of the tetA gene, encoding the tetracycline/H+ antiporter,since the ZFP represses transcription, cells that could not alleviatethis repression in the presence of the ligand would be selected againstwhen challenged with tetracycline. Therefore, growth on tetracyclinecould be used as another way to enrich for rare, ligand-responsive,biosensor variants.

Here we demonstrate that the BERDI method is capable of generating novelmetabolite-responsive biosensors from a metabolite-binding protein.Furthermore, we found that insertion of the ZFP into several positionsof this model metabolite-binding protein resulted in functionalbiosensors. Importantly, the BERDI method can potentially be generalizedto convert any metabolite binding protein, since care was taken toensure that no part of the method was specific to maltose bindingprotein by design. Finally, the BERDI method enables the development ofnovel biosensors without relying upon prior knowledge about permissivesites within the ligand-binding protein, potentially enabling thismethod to be applied to less studied proteins. Ultimately, this approachcould potentially be extended to generate novel biosensors fromvirtually any candidate metabolite binding protein for a range ofapplications in microbiology and synthetic biology.

Abbreviations

MBP, Maltose binding protein; ZFP, Zinc finger protein; IPTG, Isopropylβ-D-1-thiogalactopyranoside; LB, Lysogeny broth; OD, Optical density at600 nm; NGS, Next generation sequencing; GFP, Green fluorescent protein;TAE, tris acetate EDTA; EDTA, ethylenediaminetetraacetic acid; bla, TEM1β-lactamase.

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In the foregoing description, it will be readily apparent to one skilledin the art that varying substitutions and modifications may be made tothe invention disclosed herein without departing from the scope andspirit of the invention. The invention illustratively described hereinsuitably may be practiced in the absence of any element or elements,limitation or limitations which is not specifically disclosed herein.The terms and expressions which have been employed are used as terms ofdescription and not of limitation, and there is no intention that in theuse of such terms and expressions of excluding any equivalents of thefeatures shown and described or portions thereof, but it is recognizedthat various modifications are possible within the scope of theinvention. Thus, it should be understood that although the presentinvention has been illustrated by specific embodiments and optionalfeatures, modification and/or variation of the concepts herein disclosedmay be resorted to by those skilled in the art, and that suchmodifications and variations are considered to be within the scope ofthis invention.

Citations to a number of patent and non-patent references are madeherein. The cited references are incorporated by reference herein intheir entireties. In the event that there is an inconsistency between adefinition of a term in the specification as compared to a definition ofthe term in a cited reference, the term should be interpreted based onthe definition in the specification.

We claim:
 1. A system comprising: (a) a fusion protein that functions asa biosensor, the fusion protein comprising a ligand-binding protein(LBP) and a DNA-binding protein (DBP), the fusion protein comprising anamino acid sequence represented as: (N-terminal portion of theLBP)-(DBP)-(C-terminal portion of the LBP); and (b) a promoter which canbe operably linked to a reporter gene, wherein the promoter comprises atleast one heterologous binding site that is specific for the DBP and thefusion protein binds to the ligand and modulates expression from thepromoter.
 2. The system of claim 1, wherein the fusion protein has afirst binding affinity (K_(d1)) for the promoter in the absence of theligand, and the fusion protein has a second binding affinity (K_(d2))for the promoter in the presence of the ligand, such that K_(d1)<K_(d2)and transcription of the reporter gene is de-repressed or activated inthe presence of the ligand.
 3. The system of claim 2, whereinde-repression or activation is proportional to concentration of theligand in the system.
 4. The system of claim 2, wherein K_(d1) is <about1 nM, 0.5 nM, 0.2 nM, 0.1 nM, 0.05 nM, 0.02 nM, 0.01 nM or lower.
 5. Thesystem of claim 2, wherein K_(d2) is >about 0.02 nM, 0.05 nM, 0.1 nM,0.2 nM, 0.5 nM, 1 nM, 2 nM or higher.
 6. The system of claim 2, whereinthe ratio K_(d2):K_(d1) is at least about 5, 10, 20, 50, 100, 500, 1000or more.
 7. The system of claim 1, wherein the fusion protein has afirst binding affinity (K_(d1)) for the promoter in the absence of theligand, and the fusion protein has a second binding affinity (K_(d2))for the promoter in the presence of the ligand, such that K_(d1)>K_(d2)and transcription of the reporter gene is repressed or de-activated inthe presence of the ligand.
 8. The system of claim 7, wherein repressionor de-activation is proportional to concentration of the ligand in thesystem.
 9. The system of claim 7, wherein K_(d2) is <about 1 nM, 0.5 nM,0.2 nM, 0.1 nM, 0.05 nM, 0.02 nM, 0.01 nM or lower.
 10. The system ofclaim 7, wherein K_(d1) is >about 0.02 nM, 0.05 nM, 0.1 nM, 0.2 nM, 0.5nM, 1 nM, 2 nM or higher.
 11. The system of claim 7, wherein the ratioK_(d1):K_(d2) is at least about 5, 10, 20, 50, 100, 500, 1000 or more.12. The system of claim 1, wherein the N-terminal portion of the LBPwithin the fusion protein comprises an amino acid sequence of at leastabout 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 contiguous amino acidsfrom the N-terminus of the LBP.
 13. The system of claim 1, wherein theC-terminal portion of the LBP within the fusion protein comprises anamino acid sequence of at least about 10, 20, 30, 40, 50, 60, 70, 80,90, or 100 contiguous amino acids from the C-terminus of the LBP. 14.The system of claim 1, wherein the ligand is a cellular metabolite. 15.The system of claim 1, wherein the LBP is maltose binding protein. 16.The system of claim 1, wherein the DBP comprises one or more DNA-bindingdomains selected from the group consisting of a zinc-finger protein(ZFP) DNA-binding domain, a transcription activator-like effector (TALE)DNA-binding domain, and a clustered regularly interspaced shortpalindromic repeat (CRISPR) DNA-binding domain.
 17. The system of claim16, wherein the ZFP is BCR-ABL1.
 18. The system of claim 1, wherein thepromoter is a prokaryotic promoter.
 19. The system of claim 18, whereinthe promoter comprises two or more binding sites for the DBP.
 20. Thesystem of claim 19, wherein the binding sites are located at one or morepositions selected from: (i) between the −10 box (TATA box) and the −35box (GC-rich region); (ii) adjacent to the −10 box (TATA box) or within5 nucleotides of the −10 box (TATA box); and (iii) adjacent to the −35box (GC-rich region) or within 5 nucleotide of the −35 box (GC-richregion).
 21. A method for preparing a fusion protein for use as abiosensor, the fusion protein comprising a ligand-binding protein (LBP)and a DNA-binding protein (DBP) or portions or fragments thereof, wherethe fusion protein comprises an amino acid sequence represented as(N-terminal portion of the LBP)-(DBP)-(C-terminal portion of the LBP),the method comprising preparing a library of fusion proteins byinserting the DBP randomly into the amino acid sequence of the LBP viaperforming a recombinant DNA method, and selecting a fusion protein fromthe library of fusion proteins as a biosensor.
 22. The method of claim21, wherein the recombinant DNA method is transposon-mediated DNArecombination.
 23. The method of claim 21, wherein the fusion protein isselected as a biosensor via testing whether the fusion protein modulatestranscription from a promoter comprising a binding site for the DBP ofthe fusion protein in the presence or absence of the ligand for the LBPof the fusion protein.